Contents

Part I
GATE Basics [#]

Chapter 1
Introduction [#]

GATE² is an infrastructure for developing and deploying software components that process human language. It is nearly 15 years old and is in active use for all types of computational task involving human language. GATE excels at text analysis of all shapes and sizes. From large corporations to small startups, from €multi-million research consortia to undergraduate projects, our user community is the largest and most diverse of any system of this type, and is spread across all but one of the continents³.

GATE is open source free software; users can obtain free support from the user and developer community via GATE.ac.uk or on a commercial basis from our industrial partners. We are the biggest open source language processing project with a development team more than double the size of the largest comparable projects (many of which are integrated with GATE⁴). More than €5 million has been invested in GATE development⁵; our objective is to make sure that this continues to be money well spent for all GATE’s users.

GATE has grown over the years to include a desktop client for developers, a workflow-based web application, a Java library, an architecture and a process. GATE is:

• an IDE, GATE Developer⁶: an integrated development environment for language processing components bundled with a very widely used Information Extraction system and a comprehensive set of other plugins
• a web app, GATE Teamware: a collaborative annotation environment for factory-style semantic annotation projects built around a workflow engine and a heavily-optimised backend service infrastructure
• a framework, GATE Embedded: an object library optimised for inclusion in diverse applications giving access to all the services used by GATE Developer and more
• an architecture: a high-level organisational picture of how language processing software composition
• a process for the creation of robust and maintainable services.

We also develop:

• a wiki/CMS, GATE Wiki (http://gatewiki.sf.net/), mainly to host our own websites and as a testbed for some of our experiments
• a cloud computing solution for hosted large-scale text processing, GATE Cloud (http://gatecloud.net/)

For more information see the family pages.

One of our original motivations was to remove the necessity for solving common engineering problems before doing useful research, or re-engineering before deploying research results into applications. Core functions of GATE take care of the lion’s share of the engineering:

• modelling and persistence of specialised data structures
• measurement, evaluation, benchmarking (never believe a computing researcher who hasn’t measured their results in a repeatable and open setting!)
• visualisation and editing of annotations, ontologies, parse trees, etc.
• a finite state transduction language for rapid prototyping and efficient implementation of shallow analysis methods (JAPE)
• extraction of training instances for machine learning
• pluggable machine learning implementations (Weka, SVM Light, ...)

On top of the core functions GATE includes components for diverse language processing tasks, e.g. parsers, morphology, tagging, Information Retrieval tools, Information Extraction components for various languages, and many others. GATE Developer and Embedded are supplied with an Information Extraction system (ANNIE) which has been adapted and evaluated very widely (numerous industrial systems, research systems evaluated in MUC, TREC, ACE, DUC, Pascal, NTCIR, etc.). ANNIE is often used to create RDF or OWL (metadata) for unstructured content (semantic annotation).

GATE version 1 was written in the mid-1990s; at the turn of the new millennium we completely rewrote the system in Java; version 5 was released in June 2009. We believe that GATE is the leading system of its type, but as scientists we have to advise you not to take our word for it; that’s why we’ve measured our software in many of the competitive evaluations over the last decade-and-a-half (MUC, TREC, ACE, DUC and more; see Section 1.4 for details). We invite you to give it a try, to get involved with the GATE community, and to contribute to human language science, engineering and development.

This book describes how to use GATE to develop language processing components, test their performance and deploy them as parts of other applications. In the rest of this chapter:

• Section 1.1 describes the best way to use this book;
• Section 1.2 briefly notes that the context of GATE is applied language processing, or Language Engineering;
• Section 1.3 gives an overview of developing using GATE;
• Section 1.4 lists publications describing GATE performance in evaluations;
• Section 1.5 outlines what is new in the current version of GATE;
• Section 1.6 lists other publications about GATE.

Note: if you don’t see the component you need in this document, or if we mention a component that you can’t see in the software, contact gate-users@lists.sourceforge.net⁷ – various components are developed by our collaborators, who we will be happy to put you in contact with. (Often the process of getting a new component is as simple as typing the URL into GATE Developer; the system will do the rest.)

1.1 How to Use this Text [#]

The material presented in this book ranges from the conceptual (e.g. ‘what is software architecture?’) to practical instructions for programmers (e.g. how to deal with GATE exceptions) and linguists (e.g. how to write a pattern grammar). Furthermore, GATE’s highly extensible nature means that new functionality is constantly being added in the form of new plugins. Important functionality is as likely to be located in a plugin as it is to be integrated into the GATE core. This presents something of an organisational challenge. Our (no doubt imperfect) solution is to divide this book into three parts. Part I covers installation, using the GATE Developer GUI and using ANNIE, as well as providing some background and theory. We recommend the new user to begin with Part I. Part II covers the more advanced of the core GATE functionality; the GATE Embedded API and JAPE pattern language among other things. Part III provides a reference for the numerous plugins that have been created for GATE. Although ANNIE provides a good starting point, the user will soon wish to explore other resources, and so will need to consult this part of the text. We recommend that Part III be used as a reference, to be dipped into as necessary. In Part III, plugins are grouped into broad areas of functionality.

1.2 Context [#]

GATE can be thought of as a Software Architecture for Language Engineering [Cunningham 00].

‘Software Architecture’ is used rather loosely here to mean computer infrastructure for software development, including development environments and frameworks, as well as the more usual use of the term to denote a macro-level organisational structure for software systems [Shaw & Garlan 96].

Language Engineering (LE) may be defined as:

The relevant scientific results in this case are the outputs of Computational Linguistics, Natural Language Processing and Artificial Intelligence in general. Unlike these other disciplines, LE, as an engineering discipline, entails predictability, both of the process of constructing LE-based software and of the performance of that software after its completion and deployment in applications.

Some working definitions:

1. Computational Linguistics (CL): science of language that uses computation as an investigative tool.
2. Natural Language Processing (NLP): science of computation whose subject matter is data structures and algorithms for computer processing of human language.
3. Language Engineering (LE): building NLP systems whose cost and outputs are measurable and predictable.
4. Software Architecture: macro-level organisational principles for families of systems. In this context is also used as infrastructure.
5. Software Architecture for Language Engineering (SALE): software infrastructure, architecture and development tools for applied CL, NLP and LE.

(Of course the practice of these fields is broader and more complex than these definitions.)

In the scientific endeavours of NLP and CL, GATE’s role is to support experimentation. In this context GATE’s significant features include support for automated measurement (see Chapter 10), providing a ‘level playing field’ where results can easily be repeated across different sites and environments, and reducing research overheads in various ways.

1.3 Overview [#]

1.3.1 Developing and Deploying Language Processing Facilities [#]

GATE as an architecture suggests that the elements of software systems that process natural language can usefully be broken down into various types of component, known as resources⁸. Components are reusable software chunks with well-defined interfaces, and are a popular architectural form, used in Sun’s Java Beans and Microsoft’s .Net, for example. GATE components are specialised types of Java Bean, and come in three flavours:

• LanguageResources (LRs) represent entities such as lexicons, corpora or ontologies;
• ProcessingResources (PRs) represent entities that are primarily algorithmic, such as parsers, generators or ngram modellers;
• VisualResources (VRs) represent visualisation and editing components that participate in GUIs.

These definitions can be blurred in practice as necessary.

Collectively, the set of resources integrated with GATE is known as CREOLE: a Collection of REusable Objects for Language Engineering. All the resources are packaged as Java Archive (or ‘JAR’) files, plus some XML configuration data. The JAR and XML files are made available to GATE by putting them on a web server, or simply placing them in the local file space. Section 1.3.2 introduces GATE’s built-in resource set.

When using GATE to develop language processing functionality for an application, the developer uses GATE Developer and GATE Embedded to construct resources of the three types. This may involve programming, or the development of Language Resources such as grammars that are used by existing Processing Resources, or a mixture of both. GATE Developer is used for visualisation of the data structures produced and consumed during processing, and for debugging, performance measurement and so on. For example, figure 1.1 is a screenshot of one of the visualisation tools.

GATE Developer is analogous to systems like Mathematica for Mathematicians, or JBuilder for Java programmers: it provides a convenient graphical environment for research and development of language processing software.

When an appropriate set of resources have been developed, they can then be embedded in the target client application using GATE Embedded. GATE Embedded is supplied as a series of JAR files.⁹ To embed GATE-based language processing facilities in an application, these JAR files are all that is needed, along with JAR files and XML configuration files for the various resources that make up the new facilities.

1.3.2 Built-In Components [#]

GATE includes resources for common LE data structures and algorithms, including documents, corpora and various annotation types, a set of language analysis components for Information Extraction and a range of data visualisation and editing components.

GATE supports documents in a variety of formats including XML, RTF, email, HTML, SGML and plain text. In all cases the format is analysed and converted into a single unified model of annotation. The annotation format is a modified form the TIPSTER format [Grishman 97] which has been made largely compatible with the Atlas format [Bird & Liberman 99], and uses the now standard mechanism of ‘stand-off markup’. GATE documents, corpora and annotations are stored in databases of various sorts, visualised via the development environment, and accessed at code level via the framework. See Chapter 5 for more details of corpora etc.

A family of Processing Resources for language analysis is included in the shape of ANNIE, A Nearly-New Information Extraction system. These components use finite state techniques to implement various tasks from tokenisation to semantic tagging or verb phrase chunking. All ANNIE components communicate exclusively via GATE’s document and annotation resources. See Chapter 6 for more details. Other CREOLE resources are described in Part III.

1.3.3 Additional Facilities [#]

Three other facilities in GATE deserve special mention:

• JAPE, a Java Annotation Patterns Engine, provides regular-expression based pattern/action rules over annotations – see Chapter 8.
• The ‘annotation diff’ tool in the development environment implements performance metrics such as precision and recall for comparing annotations. Typically a language analysis component developer will mark up some documents by hand and then use these along with the diff tool to automatically measure the performance of the components. See Chapter 10.
• GUK, the GATE Unicode Kit, fills in some of the gaps in the JDK’s¹⁰ support for Unicode, e.g. by adding input methods for various languages from Urdu to Chinese. See Section 3.10.2 for more details.

And by version 4 it will make a mean cup of tea.

1.3.4 An Example [#]

This section gives a very brief example of a typical use of GATE to develop and deploy language processing capabilities in an application, and to generate quantitative results for scientific publication.

Let’s imagine that a developer called Fatima is building an email client¹¹ for Cyberdyne Systems’ large corporate Intranet. In this application she would like to have a language processing system that automatically spots the names of people in the corporation and transforms them into mailto hyperlinks.

A little investigation shows that GATE’s existing components can be tailored to this purpose. Fatima starts up GATE Developer, and creates a new document containing some example emails. She then loads some processing resources that will do named-entity recognition (a tokeniser, gazetteer and semantic tagger), and creates an application to run these components on the document in sequence. Having processed the emails, she can see the results in one of several viewers for annotations.

The GATE components are a decent start, but they need to be altered to deal specially with people from Cyberdyne’s personnel database. Therefore Fatima creates new ‘cyber-’ versions of the gazetteer and semantic tagger resources, using the ‘bootstrap’ tool. This tool creates a directory structure on disk that has some Java stub code, a Makefile and an XML configuration file. After several hours struggling with badly written documentation, Fatima manages to compile the stubs and create a JAR file containing the new resources. She tells GATE Developer the URL of these files¹², and the system then allows her to load them in the same way that she loaded the built-in resources earlier on.

Fatima then creates a second copy of the email document, and uses the annotation editing facilities to mark up the results that she would like to see her system producing. She saves this and the version that she ran GATE on into her serial datastore. From now on she can follow this routine:

1. Run her application on the email test corpus.
2. Check the performance of the system by running the ‘annotation diff’ tool to compare her manual results with the system’s results. This gives her both percentage accuracy figures and a graphical display of the differences between the machine and human outputs.
3. Make edits to the code, pattern grammars or gazetteer lists in her resources, and recompile where necessary.
4. Tell GATE Developer to re-initialise the resources.
5. Go to 1.

To make the alterations that she requires, Fatima re-implements the ANNIE gazetteer so that it regenerates itself from the local personnel data. She then alters the pattern grammar in the semantic tagger to prioritise recognition of names from that source. This latter job involves learning the JAPE language (see Chapter 8), but as this is based on regular expressions it isn’t too difficult.

Eventually the system is running nicely, and her accuracy is 93% (there are still some problem cases, e.g. when people use nicknames, but the performance is good enough for production use). Now Fatima stops using GATE Developer and works instead on embedding the new components in her email application using GATE Embedded. This application is written in Java, so embedding is very easy¹³: the GATE JAR files are added to the project CLASSPATH, the new components are placed on a web server, and with a little code to do initialisation, loading of components and so on, the job is finished in half a day – the code to talk to GATE takes up only around 150 lines of the eventual application, most of which is just copied from the example in the sheffield.examples.StandAloneAnnie class.

Because Fatima is worried about Cyberdyne’s unethical policy of developing Skynet to help the large corporates of the West strengthen their strangle-hold over the World, she wants to get a job as an academic instead (so that her conscience will only have to cope with the torture of students, as opposed to humanity). She takes the accuracy measures that she has attained for her system and writes a paper for the Journal of Nasturtium Logarithm Incitement describing the approach used and the results obtained. Because she used GATE for development, she can cite the repeatability of her experiments and offer access to example binary versions of her software by putting them on an external web server.

And everybody lived happily ever after.

1.4 Some Evaluations [#]

This section contains an incomplete list of publications describing systems that used GATE in competitive quantitative evaluation programmes. These programmes have had a significant impact on the language processing field and the widespread presence of GATE is some measure of the maturity of the system and of our understanding of its likely performance on diverse text processing tasks.

[Li et al. 07d]

describes the performance of an SVM-based learning system in the NTCIR-6 Patent Retrieval Task. The system achieved the best result on two of three measures used in the task evaluation, namely the R-Precision and F-measure. The system obtained close to the best result on the remaining measure (A-Precision).

[Saggion 07]

describes a cross-source coreference resolution system based on semantic clustering. It uses GATE for information extraction and the SUMMA system to create summaries and semantic representations of documents. One system configuration ranked 4th in the Web People Search 2007 evaluation.

[Saggion 06]

describes a cross-lingual summarization system which uses SUMMA components and the Arabic plugin available in GATE to produce summaries in English from a mixture of English and Arabic documents.

Open-Domain Question Answering:

The University of Sheffield has a long history of research into open-domain question answering. GATE has formed the basis of much of this research resulting in systems which have ranked highly during independent evaluations since 1999. The first successful question answering system developed at the University of Sheffield was evaluated as part of TREC 8 and used the LaSIE information extraction system (the forerunner of ANNIE) which was distributed with GATE [Humphreys et al. 99]. Further research was reported in [Scott & Gaizauskas. 00], [Greenwood et al. 02], [Gaizauskas et al. 03], [Gaizauskas et al. 04] and [Gaizauskas et al. 05]. In 2004 the system was ranked 9th out of 28 participating groups.

[Saggion 04]

describes techniques for answering definition questions. The system uses definition patterns manually implemented in GATE as well as learned JAPE patterns induced from a corpus. In 2004, the system was ranked 4th in the TREC/QA evaluations.

[Saggion & Gaizauskas 04b]

describes a multidocument summarization system implemented using summarization components compatible with GATE (the SUMMA system). The system was ranked 2nd in the Document Understanding Evaluation programmes.

[Maynard et al. 03e] and [Maynard et al. 03d]

describe participation in the TIDES surprise language program. ANNIE was adapted to Cebuano with four person days of effort, and achieved an F-measure of 77.5%. Unfortunately, ours was the only system participating!

[Maynard et al. 02b] and [Maynard et al. 03b]

describe results obtained on systems designed for the ACE task (Automatic Content Extraction). Although a comparison to other participating systems cannot be revealed due to the stipulations of ACE, results show 82%-86% precision and recall.

[Humphreys et al. 98]

describes the LaSIE-II system used in MUC-7.

[Gaizauskas et al. 95]

describes the LaSIE-II system used in MUC-6.

1.5 Changes in this Version [#]

This section logs changes in the latest version of GATE. Appendix A provides a complete change log.

1.5.1 February 2010

Custom JAPE operators and annotation accessors can now be loaded from plugins as well as from the lib directory (see section 8.2.2).

The GAPP packager Ant task now respects the ordering of mapping hints, with earlier hints taking precedence over later ones (see section E.2.3).

1.5.2 Version 5.1 (December 2009) [#]

Version 5.1 is a major increment with lots of new features and integration of a number of important systems from 3rd parties (e.g. LingPipe, OpenNLP, OpenCalais, a revised UIMA connector). We’ve stuck with the 5 series (instead of jumping to 6.0) because the core remains stable and backwards compatible.

Other highlights include:

• an entirely new ontology API from Johann Petrak of OFAI (the old one is still available but as a plugin)
• new benchmarking facilities for JAPE from Andrew Borthwick and colleagues at Intelius
• new quality assurance tools from Thomas Heitz and colleagues at Ontotext and Sheffield
• a generic tagger integration framework from René Witte of Concordia University
• several new code contributions from Ontotext, including a large knowledge-based gazetteer and various plugin wrappers from Marin Nozchev, Georgi Georgiev and colleagues
• a revised and reordered user guide, amalgamated with the programmers’ guide and other materials
• Groovy support, application composition, section-by-section processing and lots of other bits and pieces

New Features

LingPipe Support

LingPipe is a suite of Java libraries for the linguistic analysis of human language. We have provided a plugin called ‘LingPipe’ with wrappers for some of the resources available in the LingPipe library. For more details, see the section 19.15.

OpenNLP Support

OpenNLP provides tools for sentence detection, tokenization, pos-tagging, chunking and parsing, named-entity detection, and coreference. The tools use Maximum Entropy modelling. We have provided a plugin called ‘OpenNLP’ with wrappers for some of the resources available in the OpenNLP Tools library. For more details, see section 19.16.

OpenCalais Support

We added a new PR called ‘OpenCalais PR’. This will process a document through the OpenCalais service, and add OpenCalais entity annotations to the document. For more details, see Section 19.14.

Ontology API

The ontology API (package gate.creole.ontology has been changed, the existing ontology implementation based on Sesame1 and OWLIM2 (package gate.creole.ontology.owlim) has been moved into the plugin Ontology_OWLIM2. An upgraded implementation based on Sesame2 and OWLIM3 that also provides a number of new features has been added as plugin Ontology. See Section 14.12 for a detailed description of all changes.

Benchmarking Improvements

A number of improvements to the benchmarking support in GATE. JAPE transducers now log the time spent in individual phases of a multi-phase grammar and by individual rules within each phase. Other PRs that use JAPE grammars internally (the pronominal coreferencer, English tokeniser) log the time taken by their internal transducers. A reporting tool, called ‘Profiling Reports’ under the ‘Tools’ menu makes summary information easily available. For more details, see chapter 11.

GUI improvements

To deal with quality assurance of annotations, one component has been updated and two new components have been added. The annotation diff tool has a new mode to copy annotations to a consensus set, see section 10.2.1. An annotation stack view has been added in the document editor and it allows to copy annotations to a consensus set, see section 3.4.3. A corpus view has been added for all corpus to get statistics like precision, recall and F-measure, see section 10.3.

An annotation stack view has been added in the document editor to make easier to see overlapping annotations, see section 3.4.3.

ABNER Support

ABNER is A Biomedical Named Entity Recogniser, for finding entities such as genes in text. We have provided a plugin called ‘AbnerTagger’ with a wrapper for ABNER. For more details, see section 17.6.

Generic Tagger Support

A new plugin has been added to provide an easy route to integrate taggers with GATE. The Tagger_Framework plugin provides examples of incorporating a number of external taggers which should serve as a starting point for using other taggers. See Section 17.4 for more details.

Section-by-Section Processing

We have added a new PR called ‘Segment Processing PR’. As the name suggests this PR allows processing individual segments of a document independently of one other. For more details, please look at the section 16.2.8.

Application Composition

The gate.Controller implementations provided with the main GATE distribution now also implement the gate.ProcessingResource interface. This means that an application can now contain another application as one of its components.

Groovy Support

Groovy is a dynamic programming language based on Java. You can now use it as a scripting language for GATE, via the Groovy Console. For more details, see Section 7.15.

JAPE improvements

GATE now produces a warning when any Java right-hand-sides in JAPE rules make use of the deprecated annotations parameter. All bundled JAPE grammars have been updated to use the replacement inputAS and outputAS parameters as appropriate.

The new Imports: statement at the beginning of a JAPE grammar file can now be used to make additional Java import statements available to the Java RHS code, see 8.6.5.

The JAPE debugger has been removed. Debugging of JAPE has been made easier as stack traces now refer to the JAPE source file and line numbers instead of the generated Java source code.

The Montreal Transducer has been made obsolete.

Other improvements and bug fixes

Plugin names have been rationalised. Mappings exist so that existing applications will continue to work, but the new names should be used in the future. Plugin name mappings are given in Appendix B. Also, the Segmenter_Chinese plugin (used to be known as chineseSegmenter plugin) is now part of the Lang_Chinese plugin.

The User Guide has been amalgamated with the Programmer’s Guide; all material can now be found in the User Guide. The ‘How-To’ chapter has been converted into separate chapters for installation, GATE Developer and GATE Embedded. Other material has been relocated to the appropriate specialist chapter.

Made Mac OS launcher 64-bit compatible. See section 2.2.1 for details.

The UIMA integration layer (Chapter 18) has been upgraded to work with Apache UIMA 2.2.2.

Oracle and PostGreSQL are no longer supported.

The MIAKT Natural Language Generation plugin has been removed.

The Minorthird plugin has been removed. Minorthird has changed significantly since this plugin was written. We will consider writing an up-to-date Minorthird plugin in the future.

A new gazetteer, Large KB Gazetteer (in the plugin ‘Gazetteer_LKB’) has been added, see Section 13.10 for details.

gate.creole.tokeniser.chinesetokeniser.ChineseTokeniser and related resources under the plugins/ANNIE/tokeniser/chinesetokeniser folder have been removed. Please refer to the Lang_Chinese plugin for resources related to the Chinese language in GATE.

Added an isInitialised() method to gate.Gate().

Added a parameter to the chemistry tagger PR (section 17.5) to allow it to operate on annotation sets other than the default one.

Plus many more smaller bugfixes...

1.6 Further Reading [#]

Lots of documentation lives on the GATE web server, including:

• movies of the system in operation;
• the main system documentation tree;
• JavaDoc API documentation;
• HTML of the source code;
• parts of the requirements analysis that version 3 was based on.

For more details about Sheffield University’s work in human language processing see the NLP group pages or A Definition and Short History of Language Engineering ([Cunningham 99a]). For more details about Information Extraction see IE, a User Guide or the GATE IE pages.

A list of publications on GATE and projects that use it (some of which are available on-line):

2009

[Bontcheva et al. 09]

is the ‘Human Language Technologies’ chapter of ‘Semantic Knowledge Management’ (John Davies, Marko Grobelnik and Dunja Mladeni eds.)

[Damljanovic et al. 09]

- to appear.

[Laclavik & Maynard 09]

reviews the current state of the art in email processing and communication research, focusing on the roles played by email in information management, and commercial and research efforts to integrate a semantic-based approach to email.

[Li et al. 09]

investigates two techniques for making SVMs more suitable for language learning tasks. Firstly, an SVM with uneven margins (SVMUM) is proposed to deal with the problem of imbalanced training data. Secondly, SVM active learning is employed in order to alleviate the difficulty in obtaining labelled training data. The algorithms are presented and evaluated on several Information Extraction (IE) tasks.

2008

[Agatonovic et al. 08]

presents our approach to automatic patent enrichment, tested in large-scale, parallel experiments on USPTO and EPO documents.

[Damljanovic et al. 08]

presents Question-based Interface to Ontologies (QuestIO) - a tool for querying ontologies using unconstrained language-based queries.

[Damljanovic & Bontcheva 08]

presents a semantic-based prototype that is made for an open-source software engineering project with the goal of exploring methods for assisting open-source developers and software users to learn and maintain the system without major effort.

[Della Valle et al. 08]

presents ServiceFinder.

[Li & Cunningham 08]

describes our SVM-based system and several techniques we developed successfully to adapt SVM for the specific features of the F-term patent classification task.

[Li & Bontcheva 08]

reviews the recent developments in applying geometric and quantum mechanics methods for information retrieval and natural language processing.

[Maynard 08]

investigates the state of the art in automatic textual annotation tools, and examines the extent to which they are ready for use in the real world.

[Maynard et al. 08a]

discusses methods of measuring the performance of ontology-based information extraction systems, focusing particularly on the Balanced Distance Metric (BDM), a new metric we have proposed which aims to take into account the more flexible nature of ontologically-based applications.

[Maynard et al. 08b]

investigates NLP techniques for ontology population, using a combination of rule-based approaches and machine learning.

[Tablan et al. 08]

presents the QuestIO system a natural language interface for accessing structured information, that is domain independent and easy to use without training.

2007

[Funk et al. 07a]

describes an ontologically based approach to multi-source, multilingual information extraction.

[Funk et al. 07b]

presents a controlled language for ontology editing and a software implementation, based partly on standard NLP tools, for processing that language and manipulating an ontology.

[Maynard et al. 07a]

proposes a methodology to capture (1) the evolution of metadata induced by changes to the ontologies, and (2) the evolution of the ontology induced by changes to the underlying metadata.

[Maynard et al. 07b]

describes the development of a system for content mining using domain ontologies, which enables the extraction of relevant information to be fed into models for analysis of financial and operational risk and other business intelligence applications such as company intelligence, by means of the XBRL standard.

[Saggion 07]

describes experiments for the cross-document coreference task in SemEval 2007. Our cross-document coreference system uses an in-house agglomerative clustering implementation to group documents referring to the same entity.

[Saggion et al. 07]

describes the application of ontology-based extraction and merging in the context of a practical e-business application for the EU MUSING Project where the goal is to gather international company intelligence and country/region information.

[Li et al. 07a]

introduces a hierarchical learning approach for IE, which uses the target ontology as an essential part of the extraction process, by taking into account the relations between concepts.

[Li et al. 07b]

proposes some new evaluation measures based on relations among classification labels, which can be seen as the label relation sensitive version of important measures such as averaged precision and F-measure, and presents the results of applying the new evaluation measures to all submitted runs for the NTCIR-6 F-term patent classification task.

[Li et al. 07c]

describes the algorithms and linguistic features used in our participating system for the opinion analysis pilot task at NTCIR-6.

[Li et al. 07d]

describes our SVM-based system and the techniques we used to adapt the approach for the specifics of the F-term patent classification subtask at NTCIR-6 Patent Retrieval Task.

[Li & Shawe-Taylor 07]

studies Japanese-English cross-language patent retrieval using Kernel Canonical Correlation Analysis (KCCA), a method of correlating linear relationships between two variables in kernel defined feature spaces.

2006

[Aswani et al. 06]

(Proceedings of the 5th International Semantic Web Conference (ISWC2006)) In this paper the problem of disambiguating author instances in ontology is addressed. We describe a web-based approach that uses various features such as publication titles, abstract, initials and co-authorship information.

[Bontcheva et al. 06a]

‘Semantic Annotation and Human Language Technology’, contribution to ‘Semantic Web Technology: Trends and Research’ (Davies, Studer and Warren, eds.)

[Bontcheva et al. 06b]

‘Semantic Information Access’, contribution to ‘Semantic Web Technology: Trends and Research’ (Davies, Studer and Warren, eds.)

[Bontcheva & Sabou 06]

presents an ontology learning approach that 1) exploits a range of information sources associated with software projects and 2) relies on techniques that are portable across application domains.

[Davis et al. 06]

describes work in progress concerning the application of Controlled Language Information Extraction - CLIE to a Personal Semantic Wiki - Semper- Wiki, the goal being to permit users who have no specialist knowledge in ontology tools or languages to semi-automatically annotate their respective personal Wiki pages.

[Li & Shawe-Taylor 06]

studies a machine learning algorithm based on KCCA for cross-language information retrieval. The algorithm is applied to Japanese-English cross-language information retrieval.

[Maynard et al. 06]

discusses existing evaluation metrics, and proposes a new method for evaluating the ontology population task, which is general enough to be used in a variety of situation, yet more precise than many current metrics.

[Tablan et al. 06a]

describes an approach that allows users to create and edit ontologies simply by using a restricted version of the English language. The controlled language described is based on an open vocabulary and a restricted set of grammatical constructs.

[Tablan et al. 06b]

describes the creation of linguistic analysis and corpus search tools for Sumerian, as part of the development of the ETCSL.

[Wang et al. 06]

proposes an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools.

2005

[Aswani et al. 05]

(Proceedings of Fifth International Conference on Recent Advances in Natural Language Processing (RANLP2005)) It is a full-featured annotation indexing and search engine, developed as a part of the GATE. It is powered with Apache Lucene technology and indexes a variety of documents supported by the GATE.

[Bontcheva 05]

presents the ONTOSUM system which uses Natural Language Generation (NLG) techniques to produce textual summaries from Semantic Web ontologies.

[Cunningham 05]

is an overview of the field of Information Extraction for the 2nd Edition of the Encyclopaedia of Language and Linguistics.

[Cunningham & Bontcheva 05]

is an overview of the field of Software Architecture for Language Engineering for the 2nd Edition of the Encyclopaedia of Language and Linguistics.

[Dowman et al. 05a]

(Euro Interactive Television Conference Paper) A system which can use material from the Internet to augment television news broadcasts.

[Dowman et al. 05b]

(World Wide Web Conference Paper) The Web is used to assist the annotation and indexing of broadcast news.

[Dowman et al. 05c]

(Second European Semantic Web Conference Paper) A system that semantically annotates television news broadcasts using news websites as a resource to aid in the annotation process.

[Li et al. 05a]

(Proceedings of Sheffield Machine Learning Workshop) describe an SVM based IE system which uses the SVM with uneven margins as learning component and the GATE as NLP processing module.

[Li et al. 05b]

(Proceedings of Ninth Conference on Computational Natural Language Learning (CoNLL-2005)) uses the uneven margins versions of two popular learning algorithms SVM and Perceptron for IE to deal with the imbalanced classification problems derived from IE.

[Li et al. 05c]

(Proceedings of Fourth SIGHAN Workshop on Chinese Language processing (Sighan-05)) a system for Chinese word segmentation based on Perceptron learning, a simple, fast and effective learning algorithm.

[Polajnar et al. 05]

(University of Sheffield-Research Memorandum CS-05-10) User-Friendly Ontology Authoring Using a Controlled Language.

[Saggion & Gaizauskas 05]

describes experiments on content selection for producing biographical summaries from multiple documents.

[Ursu et al. 05]

(Proceedings of the 2nd European Workshop on the Integration of Knowledge, Semantic and Digital Media Technologies (EWIMT 2005))Digital Media Preservation and Access through Semantically Enhanced Web-Annotation.

[Wang et al. 05]

(Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2005)) Extracting a Domain Ontology from Linguistic Resource Based on Relatedness Measurements.

2004

[Bontcheva 04]

(LREC 2004) describes lexical and ontological resources in GATE used for Natural Language Generation.

[Bontcheva et al. 04]

(JNLE) discusses developments in GATE in the early naughties.

[Cunningham & Scott 04a]

(JNLE) is the introduction to the above collection.

[Cunningham & Scott 04b]

(JNLE) is a collection of papers covering many important areas of Software Architecture for Language Engineering.

[Dimitrov et al. 04]

(Anaphora Processing) gives a lightweight method for named entity coreference resolution.

[Li et al. 04]

(Machine Learning Workshop 2004) describes an SVM based learning algorithm for IE using GATE.

[Maynard et al. 04a]

(LREC 2004) presents algorithms for the automatic induction of gazetteer lists from multi-language data.

[Maynard et al. 04b]

(ESWS 2004) discusses ontology-based IE in the hTechSight project.

[Maynard et al. 04c]

(AIMSA 2004) presents automatic creation and monitoring of semantic metadata in a dynamic knowledge portal.

[Saggion & Gaizauskas 04a]

describes an approach to mining definitions.

[Saggion & Gaizauskas 04b]

describes a sentence extraction system that produces two sorts of multi-document summaries; a general-purpose summary of a cluster of related documents and an entity-based summary of documents related to a particular person.

[Wood et al. 04]

(NLDB 2004) looks at ontology-based IE from parallel texts.

2003

[Bontcheva et al. 03]

(NLPXML-2003) looks at GATE for the semantic web.

[Cunningham et al. 03]

(Corpus Linguistics 2003) describes GATE as a tool for collaborative corpus annotation.

[Kiryakov 03]

(Technical Report) discusses semantic web technology in the context of multimedia indexing and search.

[Manov et al. 03]

(HLT-NAACL 2003) describes experiments with geographic knowledge for IE.

[Maynard et al. 03a]

(EACL 2003) looks at the distinction between information and content extraction.

[Maynard et al. 03c]

(Recent Advances in Natural Language Processing 2003) looks at semantics and named-entity extraction.

[Maynard et al. 03e]

(ACL Workshop 2003) describes NE extraction without training data on a language you don’t speak (!).

[Saggion et al. 03a]

(EACL 2003) discusses robust, generic and query-based summarisation.

[Saggion et al. 03b]

(Data and Knowledge Engineering) discusses multimedia indexing and search from multisource multilingual data.

[Saggion et al. 03c]

(EACL 2003) discusses event co-reference in the MUMIS project.

[Tablan et al. 03]

(HLT-NAACL 2003) presents the OLLIE on-line learning for IE system.

[Wood et al. 03]

(Recent Advances in Natural Language Processing 2003) discusses using parallel texts to improve IE recall.

2002

[Baker et al. 02]

(LREC 2002) report results from the EMILLE Indic languages corpus collection and processing project.

[Bontcheva et al. 02a]

(ACl 2002 Workshop) describes how GATE can be used as an environment for teaching NLP, with examples of and ideas for future student projects developed within GATE.

[Bontcheva et al. 02b]

(NLIS 2002) discusses how GATE can be used to create HLT modules for use in information systems.

[Bontcheva et al. 02c], [Dimitrov 02a] and [Dimitrov 02b]

(TALN 2002, DAARC 2002, MSc thesis) describe the shallow named entity coreference modules in GATE: the orthomatcher which resolves pronominal coreference, and the pronoun resolution module.

[Cunningham 02]

(Computers and the Humanities) describes the philosophy and motivation behind the system, describes GATE version 1 and how well it lived up to its design brief.

[Cunningham et al. 02]

(ACL 2002) describes the GATE framework and graphical development environment as a tool for robust NLP applications.

[Dimitrov 02a, Dimitrov et al. 02]

(DAARC 2002, MSc thesis) discuss lightweight coreference methods.

[Lal 02]

(Master Thesis) looks at text summarisation using GATE.

[Lal & Ruger 02]

(ACL 2002) looks at text summarisation using GATE.

[Maynard et al. 02a]

(ACL 2002 Summarisation Workshop) describes using GATE to build a portable IE-based summarisation system in the domain of health and safety.

[Maynard et al. 02c]

(AIMSA 2002) describes the adaptation of the core ANNIE modules within GATE to the ACE (Automatic Content Extraction) tasks.

[Maynard et al. 02d]

(Nordic Language Technology) describes various Named Entity recognition projects developed at Sheffield using GATE.

[Maynard et al. 02e]

(JNLE) describes robustness and predictability in LE systems, and presents GATE as an example of a system which contributes to robustness and to low overhead systems development.

[Pastra et al. 02]

(LREC 2002) discusses the feasibility of grammar reuse in applications using ANNIE modules.

[Saggion et al. 02b] and [Saggion et al. 02a]

(LREC 2002, SPLPT 2002) describes how ANNIE modules have been adapted to extract information for indexing multimedia material.

[Tablan et al. 02]

(LREC 2002) describes GATE’s enhanced Unicode support.

Older than 2002

[Maynard et al. 01]

(RANLP 2001) discusses a project using ANNIE for named-entity recognition across wide varieties of text type and genre.

[Bontcheva et al. 00] and [Brugman et al. 99]

(COLING 2000, technical report) describe a prototype of GATE version 2 that integrated with the EUDICO multimedia markup tool from the Max Planck Institute.

[Cunningham 00]

(PhD thesis) defines the field of Software Architecture for Language Engineering, reviews previous work in the area, presents a requirements analysis for such systems (which was used as the basis for designing GATE versions 2 and 3), and evaluates the strengths and weaknesses of GATE version 1.

[Cunningham et al. 00a], [Cunningham et al. 98a] and [Peters et al. 98]

(OntoLex 2000, LREC 1998) presents GATE’s model of Language Resources, their access and distribution.

[Cunningham et al. 00b]

(LREC 2000) taxonomises Language Engineering components and discusses the requirements analysis for GATE version 2.

[Cunningham et al. 00c] and [Cunningham et al. 99]

(COLING 2000, AISB 1999) summarise experiences with GATE version 1.

[Cunningham et al. 00d] and [Cunningham 99b]

(technical reports) document early versions of JAPE (superseded by the present document).

[Gambäck & Olsson 00]

(LREC 2000) discusses experiences in the Svensk project, which used GATE version 1 to develop a reusable toolbox of Swedish language processing components.

[Maynard et al. 00]

(technical report) surveys users of GATE up to mid-2000.

[McEnery et al. 00]

(Vivek) presents the EMILLE project in the context of which GATE’s Unicode support for Indic languages has been developed.

[Cunningham 99a]

(JNLE) reviewed and synthesised definitions of Language Engineering.

[Stevenson et al. 98] and [Cunningham et al. 98b]

(ECAI 1998, NeMLaP 1998) report work on implementing a word sense tagger in GATE version 1.

[Cunningham et al. 97b]

(ANLP 1997) presents motivation for GATE and GATE-like infrastructural systems for Language Engineering.

[Cunningham et al. 96a]

(manual) was the guide to developing CREOLE components for GATE version 1.

[Cunningham et al. 96b]

(TIPSTER) discusses a selection of projects in Sheffield using GATE version 1 and the TIPSTER architecture it implemented.

[Cunningham et al. 96c, Cunningham et al. 96d, Cunningham et al. 95]

(COLING 1996, AISB Workshop 1996, technical report) report early work on GATE version 1.

[Gaizauskas et al. 96a]

(manual) was the user guide for GATE version 1.

[Gaizauskas et al. 96b, Cunningham et al. 97a, Cunningham et al. 96e]

(ICTAI 1996, TIPSTER 1997, NeMLaP 1996) report work on GATE version 1.

[Humphreys et al. 96]

(manual) describes the language processing components distributed with GATE version 1.

[Cunningham 94, Cunningham et al. 94]

(NeMLaP 1994, technical report) argue that software engineering issues such as reuse, and framework construction, are important for language processing R&D.

Chapter 2
Installing and Running GATE [#]

2.1 Downloading GATE [#]

To download GATE point your web browser at http://gate.ac.uk/download/.

2.2 Installing and Running GATE [#]

GATE will run anywhere that supports Java 5 or later, including Solaris, Linux, Mac OS X and Windows platforms. We don’t run tests on other platforms, but have had reports of successful installs elsewhere.

2.2.1 The Easy Way [#]

The easy way to install is to use one of the platform-specific installers (created using the excellent IzPack). Download a ‘platform-specific installer’ and follow the instructions it gives you. Once the installation is complete, you can start GATE Developer using gate.exe (Windows) or GATE.app (Mac) in the top-level installation directory, or gate.sh in the bin directory (other platforms).

Note for Mac users: on 64-bit-capable systems, GATE.app will run as a 64-bit application. It will use the first listed 64-bit JVM in your Java Preferences, even if your highest priority JVM is a 32-bit one. Thus if you want to run using Java 5 rather than 6 you must ensure that “J2SE 5.0 64-bit” is listed ahead of “Java SE 6 64-bit”.

2.2.2 The Hard Way (1) [#]

Download the Java-only release package or the binary build snapshot, and follow the instructions below.

Prerequisites:

• A conforming Java 2 environment,
  • version 1.4.2 or above for GATE 3.1
  • version 5.0 for GATE 4.0 beta 1 or later.

  available free from Sun Microsystems or from your UNIX supplier. (We test on various Sun JDKs on Solaris, Linux and Windows XP.)

• Binaries from the GATE distribution you downloaded: gate.jar, lib/ext/guk.jar (Unicode editing support) and a suitable script to start Ant, e.g. ant.sh or ant.bat. These are held in a directory called bin like this:
          .../bin/  
              gate.jar  
              ant.sh  
              ant.bat

  You will also need the lib directory, containing various libraries that GATE depends on.

• An open mind and a sense of humour.

Using the binary distribution:

• Unpack the distribution, creating a directory containing jar files and scripts.
• To run GATE Developer: on Windows, start a Command Prompt window, change to the directory where you unpacked the GATE distribution and run ‘bin/ant.bat run’; on UNIX or mac open a terminal window and run ‘bin/ant run’.
• To embed GATE as a library (GATE Embedded), put gate.jar and all the libraries in the lib directory in your CLASSPATH and tell Java that guk.jar is an extension (-Djava.ext.dirs=path-to-guk.jar).

The Ant scripts that start GATE Developer (ant.bat or ant) require you to set the JAVA_HOME environment variable to point to the top level directory of your JAVA installation. The value of GATE_CONFIG is passed to the system by the scripts using either a -i command-line option, or the Java property gate.config.

2.2.3 The Hard Way (2): Subversion [#]

The GATE code is maintained in a Subversion repository. You can use a Subversion client to check out the source code – the most up-to-date version of GATE is the trunk:
svn checkout https://gate.svn.sourceforge.net/svnroot/gate/gate/trunk gate

Once you have checked out the code you can build GATE using Ant (see Section 2.5)

You can browse the complete Subversion repository online at http://gate.svn.sourceforge.net/gate.

2.3 Using System Properties with GATE [#]

During initialisation, GATE reads several Java system properties in order to decide where to find its configuration files.

Here is a list of the properties used, their default values and their meanings:

gate.home

sets the location of the GATE install directory. This should point to the top level directory of your GATE installation. This is the only property that is required. If this is not set, the system will display an error message and them it will attempt to guess the correct value.

gate.plugins.home

points to the location of the directory containing installed plugins (a.k.a. CREOLE directories). If this is not set then the default value of {gate.home}/plugins is used.

gate.site.config

points to the location of the configuration file containing the site-wide options. If not set this will default to {gate.home}/gate.xml. The site configuration file must exist!

gate.user.config

points to the file containing the user’s options. If not specified, or if the specified file does not exist at startup time, the default value of gate.xml (.gate.xml on Unix platforms) in the user’s home directory is used.

gate.user.session

points to the file containing the user’s saved session. If not specified, the default value of gate.session (.gate.session on Unix) in the user’s home directory is used. When starting up GATE Developer, the session is reloaded from this file if it exists, and when exiting GATE Developer the session is saved to this file (unless the user has disabled ‘save session on exit’ in the configuration dialog). The session is not used when using GATE Embedded.

load.plugin.path

is a path-like structure, i.e. a list of URLs separated by ‘;’. All directories listed here will be loaded as CREOLE plugins during initialisation. This has similar functionality with the the -d command line option.

gate.builtin.creole.dir

is a URL pointing to the location of GATE’s built-in CREOLE directory. This is the location of the creole.xml file that defines the fundamental GATE resource types, such as documents, document format handlers, controllers and the basic visual resources that make up GATE. The default points to a location inside gate.jar and should not generally need to be overridden.

When using GATE Embedded, you can set the values for these properties before you call Gate.init(). Alternatively, you can set the values programmatically using the static methods setGateHome(), setPluginsHome(), setSiteConfigFile(), etc. before calling Gate.init(). See the Javadoc documentation for details. If you want to set these values from the command line you can use the following syntax for setting gate.home for example:

java -Dgate.home=/my/new/gate/home/directory -cp... gate.Main

When running GATE Developer, you can set the properties by creating a file build.properties in the top level GATE directory. In this file, any system properties which are prefixed with ‘run.’ will be passed to GATE. For example, to set an alternative user config file, put the following line in build.properties¹:

run.gate.user.config=${user.home}/alternative-gate.xml

This facility is not limited to the GATE-specific properties listed above, for example the following line changes the default temporary directory for GATE (note the use of forward slashes, even on Windows platforms):

run.java.io.tmpdir=d:/bigtmp

2.4 Configuring GATE [#]

When GATE Developer is started, or when Gate.init() is called from GATE Embedded, GATE loads various sorts of configuration data stored as XML in files generally called something like gate.xml or .gate.xml. This data holds information such as:

• whether to save settings on exit;
• whether to save session on exit;
• what fonts GATE Developer should use;
• plugins to load at start;
• colours of the annotations;
• locations of files for the file chooser;
• and a lot of other GUI related options;

This type of data is stored at two levels (in order from general to specific):

• the site-wide level, which by default is located the gate.xml file in top level directory of the GATE installation (i.e. the GATE home. This location can be overridden by the Java system property gate.site.config;
• the user level, which lives in the user’s HOME directory on UNIX or their profile directory on Windows (note that parts of this file are overwritten when saving user settings). The default location for this file can be overridden by the Java system property gate.user.config.

Where configuration data appears on several different levels, the more specific ones overwrite the more general. This means that you can set defaults for all GATE users on your system, for example, and allow individual users to override those defaults without interfering with others.

Configuration data can be set from the GATE Developer GUI via the ‘Options’ menu then ‘Configuration’. The user can change the appearance of the GUI in the ‘Appearance’ tab, which includes the options of font and the ‘look and feel’. The ‘Advanced’ tab enables the user to include annotation features when saving the document and preserving its format, to save the selected Options automatically on exit, and to save the session automatically on exit. The ‘Input Methods’ submenu from the ‘Options’ menu enables the user to change the default language for input. These options are all stored in the user’s .gate.xml file.

When using GATE Embedded, you can also set the site config location using Gate.setSiteConfigFile(File) prior to calling Gate.init().

2.5 Building GATE [#]

Note that you don’t need to build GATE unless you’re doing development on the system itself.

Prerequisites:

• A conforming Java environment as above.
• A copy of the GATE sources and the build scripts – either the SRC distribution package from the nightly snapshots or a copy of the code obtained through Subversion (see Section 2.2.3).
• An appreciation of natural beauty.

GATE now includes a copy of the ANT build tool which can be accessed through the scripts included in the bin directory (use ant.bat for Windows 98 or ME, ant.cmd for Windows NT, 2000 or XP, and ant.sh for Unix platforms).

To build gate, cd to gate and:

1. Type:
   bin/ant
2. [optional] To test the system:
   bin/ant test
   
3. [optional] To make the Javadoc documentation:
   bin/ant doc
   
4. You can also run GATE Developer using Ant, by typing:
   bin/ant run
5. To see a full list of options type: bin/ant help
   

(The details of the build process are all specified by the build.xml file in the gate directory.)

You can also use a development environment like Borland JBuilder (click on the gate.jpx file), but note that it’s still advisable to use ant to generate documentation, the jar file and so on. Also note that the run configurations have the location of a gate.xml site configuration file hard-coded into them, so you may need to change these for your site.

2.5.1 Using GATE with Maven or JPF [#]

This section is based on contributions by Georg Öttl and William Oberman.

To use GATE with Maven you need a definition of the dependencies in POM format. There’s an example POM here.

To use GATE with JPF (a Java plugin framework) you need a plugin definition like this one.

2.6 Uninstalling GATE [#]

If you have used the installer, run:

or just delete the whole of the installation directory (the one containing bin, lib, Uninstaller, etc.). The installer doesn’t install anything outside this directory, but for completeness you might also want to delete the settings files GATE creates in your home directory (.gate.xml and .gate.session).

2.7 Troubleshooting [#]

2.7.1 I don’t see the Java console messages under Windows [#]

Note that the gate.bat script uses javaw.exe to run GATE which means that you will see no console for the java process. If you have problems starting GATE and you would like to be able to see the console to check for messages then you should edit the gate.bat script and replace javaw.exe with java.exe in the definition of the JAVA environment variable.

2.7.2 When I execute GATE, nothing happens [#]

You might get some clues if you start GATE from the command line, using:

which will allow you to see all error messages GATE generates.

2.7.3 On Ubuntu, GATE is very slow or doesn’t start [#]

GATE and many other Java applications are known not to work with GCJ, the open-source Java SDK or others non SUN Java SDK.

Make sure you have the official version of Java installed. Provided by Sun, the package is named ‘sun-java6-jdk’ in Synaptic. GATE also works with Java version 5 so ‘sun-java5-jdk’.

To install it, run in a terminal:

Make sure that your default Java version is the one from SUN. You can do this by running:

This will list the installed Java VMs. You should see ‘java-6-sun’ as one of the options.

Then you should run :

to set the ‘java-6-sun’ as your default.

Finally, try GATE again.

2.7.4 How to use GATE on a 64 bit system? [#]

32-bit vs. 64-bit is a matter of the JVM rather than the build of GATE -

For example, on Mac OS X, either use Applications/Utilities/Java Preferences and put one of the 64-bit options at the top of the list, or run GATE from the terminal using Java 1.6.0 (which is 64-bit only on Mac OS):

2.7.5 I got the error: Could not reserve enough space for object heap [#]

GATE doesn’t use the JAVA_OPTS variable. The default memory allocations are defined in the gate/build.xml file but you can override them by creating a file called build.properties in the same directory containing

2.7.6 I got the error: log4j:WARN No appenders could be found for logger... [#]

You need to copy the ‘gate/bin/log4j.properties’ file to the directory from which you execute your project.

2.7.7 Text is incorrectly refreshed after scrolling and become unreadable [#]

Change the look and feel used in GATE with menu ‘Options’ then ‘Configuration’. Restart GATE and try again. We use mainly ‘Metal’ and ‘Nimbus’ without problem.

Change the video driver you use.

Update Java.

Chapter 3
Using GATE Developer [#]

This chapter introduces GATE Developer, which is the GATE graphical user interface. It is analogous to systems like Mathematica for mathematicians, or Eclipse for Java programmers, providing a convenient graphical environment for research and development of language processing software. As well as being a powerful research tool in its own right, it is also very useful in conjunction with GATE Embedded (the GATE API by which GATE functionality can be included in your own applications); for example, GATE Developer can be used to create applications that can then be embedded via the API. This chapter describes how to complete common tasks using GATE Developer. It is intended to provide a good entry point to GATE functionality, and so explanations are given assuming only basic knowledge of GATE. However, probably the best way to learn how to use GATE Developer is to use this chapter in conjunction with the demonstrations and tutorials movies. There are specific links to them throughout the chapter. There is also a complete new set of video tutorials here.

The basic business of GATE is annotating documents, and all the functionality we will introduce relates to that. Core concepts are;

• the documents to be annotated,
• corpora comprising sets of documents, grouping documents for the purpose of running uniform processes across them,
• annotations that are created on documents,
• annotation types such as ‘Name’ or ‘Date’,
• annotation sets comprising groups of annotations,
• processing resources that manipulate and create annotations on documents, and
• applications, comprising sequences of processing resources, that can be applied to a document or corpus.

What is considered to be the end result of the process varies depending on the task, but for the purposes of this chapter, output takes the form of the annotated document/corpus. Researchers might be more interested in figures demonstrating how successfully their application compares to a ‘gold standard’ annotation set; Chapter 10 in Part II will cover ways of comparing annotation sets to each other and obtaining measures such as F1. Implementers might be more interested in using the annotations programmatically; Chapter 7, also in Part II, talks about working with annotations from GATE Embedded. For the purposes of this chapter, however, we will focus only on creating the annotated documents themselves, and creating GATE applications for future use.

GATE includes a complete information extraction system that you are free to use, called ANNIE (a Nearly-New Information Extraction System). Many users find this is a good starting point for their own application, and so we will cover it in this chapter. Chapter 6 talks in a lot more detail about the inner workings of ANNIE, but we aim to get you started using ANNIE from inside of GATE Developer in this chapter.

We start the chapter with an exploration of the GATE Developer GUI, in Section 3.1. We describe how to create documents (Section 3.2) and corpora (Section 3.3). We talk about viewing and manually creating annotations (Section 3.4).

We then talk about loading the plugins that contain the processing resources you will use to construct your application, in Section 3.5. We then talk about instantiating processing resources (Section 3.6). Section 3.7 covers applications, including using ANNIE (Section 3.7.2). Saving applications and language resources (documents and corpora) is covered in Section 3.8. We conclude with a few assorted topics that might be useful to the GATE Developer user, in Section 3.10.

3.1 The GATE Developer Main Window [#]

Figure 3.1 shows the main window of GATE Developer, as you will see it when you first run it. There are five main areas:

1. at the top, the menus bar and tools bar with menus ‘File’, ‘Options’, ‘Tools’, ‘Help’ and icons for the most frequently used actions;
2. on the left side, a tree starting from ‘GATE’ and containing ‘Applications’, ‘Language Resources’ etc. – this is the resources tree;
3. in the bottom left corner, a rectangle, which is the small resource viewer;
4. in the center, containing tabs with ‘Messages’ or the name of a resource from the resources tree, the main resource viewer;
5. at the bottom, the messages bar.

The menu and the messages bar do the usual things. Longer messages are displayed in the messages tab in the main resource viewer area.

The resource tree and resource viewer areas work together to allow the system to display diverse resources in various ways. The many resources integrated with GATE can have either a small view, a large view, or both.

At any time, the main viewer can also be used to display other information, such as messages, by clicking on the appropriate tab at the top of the main window. If an error occurs in processing, the messages tab will flash red, and an additional popup error message may also occur.

In the options dialogue from the Options menu you can choose if you want to link the selection in the resources tree and the selected main view.

3.2 Loading and Viewing Documents [#]

If you right-click on ‘Language Resources’ in the resources pane, select “New’ then ‘GATE Document’, the window ‘Parameters for the new GATE Document’ will appear as shown in figure 3.2. Here, you can specify the GATE document to be created. Required parameters are indicated with a tick. The name of the document will be created for you if you do not specify it. Enter the URL of your document or use the file browser to indicate the file you wish to use for your document source. For example, you might use ‘http://www.gate.ac.uk’, or browse to a text or XML file you have on disk. Click on ‘OK’ and a GATE document will be created from the source you specified.

See also the movie for creating documents.

The document editor is contained in the central tabbed pane in GATE Developer. Double-click on your document in the resources pane to view the document editor. The document editor consists of a top panel with buttons and icons that control the display of different views and the search box. Initially, you will see just the text of your document, as shown in figure 3.3. Click on ‘Annotation Sets’ and ‘Annotations List’ to view the annotation sets to the right and the annotations list at the bottom. You will see a view similar to figure 3.4. In place of the annotations list, you can also choose to see the annotations stack. In place of the annotation sets, you can also choose to view the co-reference editor. More information about this functionality is given in Section 3.4.

Text in a loaded document can be edited in the document viewer. The usual platform specific cut, copy and paste keyboard shortcuts should also work, depending on your operating system (e.g. CTRL-C, CTRL-V for Windows). The last icon, a magnifying glass, at the top of the document editor is for searching in the document. To prevent the new annotation windows popping up when a piece of text is selected, hold down the CTRL key. Alternatively, you can hide the annotation sets view by clicking on its button at the top of the document view; this will also cause the highlighted portions of the text to become un-highlighted.

You can set the document editor to be read-only in the options dialogue from the ‘Options’ menu. If enabled, you won’t be able to edit the text but you will still be able to edit annotations.

Another options is to choose if the insertion when editing text should be before or after the caret.

See also Section 16.2.2 for the compound document editor.

3.3 Creating and Viewing Corpora [#]

You can create a new corpus in a similar manner to creating a new document; simply right-click on ‘Language Resources’ in the resources pane, select ‘New’ then ‘GATE corpus’. A brief dialogue box will appear in which you can optionally give a name for your corpus (if you leave this blank, a corpus name will be created for you) and optionally add documents to the corpus from those already loaded into GATE.

There are three ways of adding documents to a corpus:

1. When creating the corpus, clicking on the icon next to the “documentsList” input field brings up a popup window with a list of the documents already loaded into GATE Developer. This enables the user to add any documents to the corpus.
2. Alternatively, the corpus can be loaded first, and documents added later by double clicking on the corpus and using the + and - icons to add or remove documents to the corpus. Note that the documents must have been loaded into GATE Developer before they can be added to the corpus.
3. Once loaded, the corpus can be populated by right clicking on the corpus and selecting ‘Populate’. With this method, documents do not have to have been previously loaded into GATE Developer, as they will be loaded during the population process. If you right-click on your corpus in the resources pane, you will see that you have the option to ‘Populate’ the corpus. If you select this option, you will see a dialogue box in which you can specify a directory in which GATE will search for documents. You can specify the extensions allowable; for example, XML or TXT. This will restrict the corpus population to only those documents with the extensions you wish to load. You can choose whether to recurse through the directories contained within the target directory or restrict the population to those documents contained in the top level directory. Click on ‘OK’ to populate your corpus. This option provides a quick way to create a GATE Corpus from a directory of documents.

Additionally, right-clicking on a loaded document in the tree and selecting the ‘New corpus with this document’ option creates a new transient corpus named Corpus for document name containing just this document.

See also the movie for creating and populating corpora.

Double click on your corpus in the resources pane to see the corpus editor, shown in figure 3.5. You will see a list of the documents contained within the corpus.

In the top left of the corpus editor, plus and minus buttons allow you to add documents to the corpus from those already loaded into GATE and remove documents from the corpus (note that removing a document from a corpus does not remove it from GATE).

Up and down arrows at the top of the view allow you to reorder the documents in the corpus. The rightmost button in the view opens the currently selected document in a document editor.

At the bottom, you will see that tabs entitled ‘Initialisation Parameters’ and ‘Corpus Quality Assurance’ are also available in addition to the corpus editor tab you are currently looking at. Clicking on the ‘Initialisation Parameters’ tab allows you to view the initialisation parameters for the corpus. The ‘Corpus Quality Assurance’ tab allows you to calculate agreement measures between the annotations in your corpus. Agreement measures are discussed in depth in Chapter 10. The use of corpus quality assurance is discussed in Section 10.3.

3.4 Working with Annotations [#]

In this section, we will talk in more detail about viewing annotations, as well as creating and editing them manually. As discussed in at the start of the chapter, the main purpose of GATE is annotating documents. Whilst applications can be used to annotate the documents entirely automatically, annotation can also be done manually, e.g. by the user, or semi-automatically, by running an application over the corpus and then correcting/adding new annotations manually. Section 3.4.5 focuses on manual annotation. In Section 3.6 we talk about running processing resources on our documents. We begin by outlining the functionality around viewing annotations, organised by the GUI area to which the functionality pertains.

3.4.1 The Annotation Sets View [#]

To view the annotation sets, click on the ‘Annotation Sets’ button at the top of the document editor, or use the F3 key (see Section 3.9 for more keyboard shortcuts). This will bring up the annotation sets viewer, which displays the annotation sets available and their corresponding annotation types.

The annotation sets view is displayed on the left part of the document editor. It’s a tree-like view with a root for each annotation set. The first annotation set in the list is always a nameless set. This is the default annotation set. You can see in figure 3.4 that there is a drop-down arrow with no name beside it. Other annotation sets on the document shown in figure 3.4 are ‘Key’ and ‘Original markups’. Because the document is an XML document, the original XML markup is retained in the form of an annotation set. This annotation set is expanded, and you can see that there are annotations for ‘TEXT’, ‘body’, ‘font’, ‘html’, ‘p’, ‘table’, ‘td’ and ‘tr’.

To display all the annotations of one type, tick its checkbox or use the space key. The text segments corresponding to these annotations will be highlighted in the main text window. To delete an annotation type, use the delete key. To change the color, use the enter key. There is a context menu for all these actions that you can display by right-clicking on one annotation type, a selection or an annotation set.

If you keep shift key pressed when you open the annotation sets view, GATE Developer will try to select any annotations that were selected in the previous document viewed (if any); otherwise no annotation will be selected.

Having selected an annotation type in the annotation sets view, hovering over an annotation in the main resource viewer or right-clicking on it will bring up a popup box containing a list of the annotations associated with it, from which one can select an annotation to view in the annotation editor, or if there is only one, the annotation editor for that annotation. Figure 3.6 shows the annotation editor.

3.4.2 The Annotations List View [#]

To view the annotations and their features, click on the ‘Annotations list’ button at the top or bottom of the main window or use F4 key. The annotation list viewer will appear above or below the main text, respectively. It will only contain the annotations selected from the annotation sets. These lists can be sorted in ascending and descending order by any column, by clicking on the corresponding column heading. Moreover you can hide a column by using the context menu with right-click. Clicking on an entry in the table will also highlight the respective matching text portion. Right-click on a row in this view to delete or edit an annotation.

3.4.3 The Annotations Stack View [#]

This view is similar to the annotations list view, but instead of displaying all the annotations of the document, it displays only annotations at the document caret position with some context before and after. The annotations are stacked from top to bottom, which gives a clear view when they are overlapping.

As the view is centred on the document caret, you can use the conventional keypresses to move it and update the view: notably the keys left and right to skip one letter; control + left/right to skip one word; up and down to go one line up or down; and use the document scrollbar then click in the document to move further. There are also two buttons at the top of the view that centre the view on the closest previous/next annotation boundary among all displayed. This is useful when you want to skip a region without annotation or when you want to reach the beginning or end of a very long annotation.

The annotation types displayed correspond to those selected in the annotation sets view. You can display feature values for an annotation rectangle by hovering the mouse on it or select only one feature to display by double-clicking on the annotation type in the first column.

Right-clicking on an annotation in the annotations stack view gives the option to edit that annotation.

3.4.4 The Co-reference Editor [#]

The co-reference editor allows co-reference chains (see Section 6.9) to be displayed and edited in GATE Developer. To display the co-reference editor, first open a document in GATE Developer, and then click on the Co-reference Editor button in the document viewer.

The combo box at the top of the co-reference editor allows you to choose which annotation set to display co-references for. If an annotation set contains no co-reference data, then the tree below the combo box will just show ‘Coreference Data’ and the name of the annotation set. However, when co-reference data does exist, a list of all the co-reference chains that are based on annotations in the currently selected set is displayed. The name of each co-reference chain in this list is the same as the text of whichever element in the chain is the longest. It is possible to highlight all the member annotations of any chain by selecting it in the list.

When a co-reference chain is selected, if the mouse is placed over one of its member annotations, then a pop-up box appears, giving the user the option of deleting the item from the chain. If the only item in a chain is deleted, then the chain itself will cease to exist, and it will be removed from the list of chains. If the name of the chain was derived from the item that was deleted, then the chain will be given a new name based on the next longest item in the chain.

A combo box near the top of the co-reference editor allows the user to select an annotation type from the current set. When the Show button is selected all the annotations of the selected type will be highlighted. Now when the mouse pointer is placed over one of those annotations, a pop-up box will appear giving the user the option of adding the annotation to a co-reference chain. The annotation can be added to an existing chain by typing the name of the chain (as shown in the list on the right) in the pop-up box. Alternatively, if the user presses the down cursor key, a list of all the existing annotations appears, together with the option [New Chain]. Selecting the [New Chain] option will cause a new chain to be created containing the selected annotation as its only element.

Each annotation can only be added to a single chain, but annotations of different types can be added to the same chain, and the same text can appear in more than one chain if it is referenced by two or more annotations.

The movie for inspecting results is also useful for learning about viewing annotations.

3.4.5 Creating and Editing Annotations [#]

To create annotations manually, select the text you want to annotate and hover the mouse on the selection. A popup will appear, allowing you to create an annotation, as shown in figure 3.9

The type of the annotation, by default, will be the same as the last annotation you created, unless there is none, in which case it will be ‘_New_’. You can enter any annotation type name you wish in the text box, unless you are using schema-driven annotation (see Section 3.4.6). You can add or change features and their values in the table below.

To delete an annotation, click on the red X icon at the top of the popup window. To grow/shrink the span of the annotation at its start use the two arrow icons on the left or right and left keys. Use the two arrow icons next on the right to change the annotation end or alt+right and alt+left keys. Add shift and control+shift keys to make the span increment bigger. The red X icon is for removing the annotation.

The pin icon is to pin the window so that it remains where it is. If you drag and drop the window, this automatically pins it too. Pinning it means that even if you select another annotation (by hovering over it in the main resource viewer) it will still stay in the same position.

The popup menu only contains annotation types present in the Annotation Schema and those already listed in the relevant Annotation Set. To create a new Annotation Schema, see Section 3.4.6. The popup menu can be edited to add a new annotation type, however.

The new annotation created will automatically be placed in the annotation set that has been selected (highlighted) by the user. To create a new annotation set, type the name of the new set to be created in the box below the list of annotation sets, and click on ‘New’.

Figure 3.10 demonstrates adding a ‘Organization’ annotation for the string ‘EPSRC’ (highlighted in green) to the default annotation set (blank name in the annotation set view on the right) and a feature name ‘type’ with a value about to be added.

To add a second annotation to a selected piece of text, or to add an overlapping annotation to an existing one, press the CTRL key to avoid the existing annotation popup appearing, and then select the text and create the new annotation. Again by default the last annotation type to have been used will be displayed; change this to the new annotation type. When a piece of text has more than one annotation associated with it, on mouseover all the annotations will be displayed. Selecting one of them will bring up the relevant annotation popup.

To search and annotate the document automatically, use the search and annotate function as shown in figure 3.11:

• Create and/or select an annotation to be used as a model to annotate.
• Open the panel at the bottom of the annotation editor window.
• Change the expression to search if necessary.
• Use the [First] button or Enter key to select the first expression to annotate.
• Use the [Annotate] button if the selection is correct otherwise the [Next] button. After a few cycles of [Annotate] and [Next], Use the [Ann. all next] button.

Note that after using the [First] button you can move the caret in the document and use the [Next] button to avoid continuing the search from the beginning of the document. The [?] button at the end of the search text field will help you to build powerful regular expressions to search.

3.4.6 Schema-Driven Editing [#]

Annotation schemas allow annotation types and features to be pre-specified, so that during manual annotation, the relevant options appear on the drop-down lists in the annotation editor. You can see some example annotation schemas in Section 5.4.1. Annotation schemas provide a means to define types of annotations in GATE Developer. Basically this means that GATE Developer ‘knows about’ annotations defined in a schema.

Annotation schemas are supported by the ‘Annotation schema’ language resource in ANNIE, so to use them you must first ensure that the ‘ANNIE’ plugin is loaded (see Section 3.5). This will load a set of default schemas, as well as allowing you to load schemas of your own.

The default annotation schemas contain common named entities such as Person, Organisation, Location, etc. You can modify the existing schema or create a new one, in order to tell GATE Developer about other kinds of annotations you frequently use. You can still create annotations in GATE Developer without having specified them in an annotation schema, but you may then need to tell GATE Developer about the properties of that annotation type each time you create an annotation for it.

To load a schema of your own, right-click on ‘Language Resources’ in the resources pane. Select ‘New’ then ‘Annotation schema’. A popup box will appear in which you can browse to your annotation schema XML file.

An alternative annotation editor component is available which constrains the available annotation types and features much more tightly, based on the annotation schemas that are currently loaded. This is particularly useful when annotating large quantities of data or for use by less skilled users.

To use this, you must load the Schema_Annotation_Editor plugin. With this plugin loaded, the annotation editor will only offer the annotation types permitted by the currently loaded set of schemas, and when you select an annotation type only the features permitted by the schema are available to edit¹. Where a feature is declared as having an enumerated type the available enumeration values are presented as an array of buttons, making it easy to select the required value quickly.

3.4.7 Printing Text with Annotations [#]

We suggest you to use your browser to print a document as GATE don’t propose a printing facility for the moment.

First save your document by right clicking on the document in the left resources tree then choose ‘Save Preserving Format’. You will get an XML file with all the annotations highlighted as XML tags plus the ‘Original markups’ annotations set.

Then add a stylesheet processing instruction at the beginning of the XML file:

And create a file ‘gate.css’ in the same directory:

Finally open the XML file in your browser and print it.

Note that overlapping annotations, cannot be expressed correctly with inline XML tags and thus won’t be displayed correctly.

3.5 Using CREOLE Plugins [#]

In GATE, processing resources are used to automatically create and manipulate annotations on documents. We will talk about processing resources in the next section. However, we must first introduce CREOLE plugins. In most cases, in order to use a particular processing resource (and certain language resources) you must first load the CREOLE plugin that contains it. This section talks about using CREOLE plugins. Then, in Section 3.6, we will talk about creating and using processing resources.

The definitions of CREOLE resources (e.g. processing resources such as taggers and parsers, see Chapter 4) are stored in CREOLE directories (directories containing an XML file describing the resources, the Java archive with the compiled executable code and whatever libraries are required by the resources).

Starting with version 3, CREOLE directories are called ‘CREOLE plugins’ or simply ‘plugins’. In previous versions, the CREOLE resources distributed with GATE used to be included in the monolithic gate.jar archive. Version 3 includes them as separate directories under the plugins directory of the distribution. This allows easy access to the linguistic resources used without the requirement to unpack the gate.jar file.

Plugins can have one or more of the following states in relation with GATE:

known

plugins are those plugins that the system knows about. These include all the plugins in the plugins directory of the GATE installation (the so–called installed plugins) as well all the plugins that were manually loaded from the user interface.

loaded

plugins are the plugins currently loaded in the system. All CREOLE resource types from the loaded plugins are available for use. All known plugins can easily be loaded and unloaded using the user interface.

auto-loadable

plugins are the list of plugins that the system loads automatically during initialisation.

The default location for installed plugins can be modified using the gate.plugins.home system property while the list of auto-loadable plugins can be set using the load.plugin.path property, see Section 2.3 above.

The CREOLE plugins can be managed through the graphical user interface which can be activated by selecting ‘Manage CREOLE Plugins’ from the ‘File’ menu. This will bring up a window listing all the known plugins. For each plugin there are two check-boxes – one labelled ‘Load now’, which will load the plugin, and the other labelled ‘Load always’ which will add the plugin to the list of auto-loadable plugins. A ‘Delete’ button is also provided – which will remove the plugin from the list of known plugins. This operation does not delete the actual plugin directory. Installed plugins are found automatically when GATE is started; if an installed plugin is deleted from the list, it will re-appear next time GATE is launched.

If you select a plugin, you will see in the pane on the right the list of resources that plugin contains. For example, in figure 3.12, the ‘Alignment’ plugin is selected, and you can see that it contains ten processing resources; ‘Compound Document’, ‘Compound Document From Xml’, ‘Compound Document Editor’, ‘GATE Composite document’ etc. If you wish to use a particular resource you will have to ascertain which plugin contains it. This list can be useful for that. Alternatively, the GATE website provides a directory of plugins and their processing resources.

Having loaded the plugins you need, the resources they define will be available for use. Typically, to the GATE Developer user, this means that they will appear on the ‘New’ menu when you right-click on ‘Processing Resources’ in the resources pane, although some special plugins have different effects; for example, the Schema_Annotation_Editor (see Section 3.4.6).

3.6 Loading and Using Processing Resources [#]

This section describes how to load and run CREOLE resources not present in ANNIE. To load ANNIE, see Section 3.7.2. For technical descriptions of these resources, see the appropriate chapter in Part III (e.g. Chapter 19). First ensure that the necessary plugins have been loaded (see Section 3.5). If the resource you require does not appear in the list of Processing Resources, then you probably do not have the necessary plugin loaded. Processing resources are loaded by selecting them from the set of Processing Resources: right click on Processing Resources or select ‘New Processing Resource’ from the File menu.

For example, use the Plugin Console Manager to load the ‘Tools’ plugin. When you right click on ‘Processing Resources’ in the resources pane and select ‘New’ you have the option to create any of the processing resources that plugin provides. You may choose to create a ‘GATE Morphological Analyser’, with the default parameters. Having done this, an instance of the GATE Morphological Analyser appears under ‘Processing Resources’. This processing resource, or PR, is now available to use. Double-clicking on it in the resources pane reveals its initialisation parameters, see figure 3.13.

This processing resource is now available to be added to applications. It must be added to an application before it can be applied to documents. You may create as many of a particular processing resource as you wish, for example with different initialisation parameters. Section 3.7 talks about creating and running applications.

See also the movie for loading processing resources.

3.7 Creating and Running an Application [#]

Once all the resources you need have been loaded, an application can be created from them, and run on your corpus. Right click on ‘Applications’ and select ‘New’ and then either ‘Corpus Pipeline’ or ‘Pipeline’. A pipeline application can only be run over a single document, while a corpus pipeline can be run over a whole corpus.

To build the pipeline, double click on it, and select the resources needed to run the application (you may not necessarily wish to use all those which have been loaded). Transfer the necessary components from the set of ‘loaded components’ displayed on the left hand side of the main window to the set of ‘selected components’ on the right, by selecting each component and clicking on the left and right arrows, or by double-clicking on each component. Ensure that the components selected are listed in the correct order for processing (starting from the top). If not, select a component and move it up or down the list using the up/down arrows at the left side of the pane. Ensure that any parameters necessary are set for each processing resource (by clicking on the resource from the list of selected resources and checking the relevant parameters from the pane below). For example, if you wish to use annotation sets other than the Default one, these must be defined for each processing resource. Note that if a corpus pipeline is used, the corpus needs only to be set once, using the drop-down menu beside the ‘corpus’ box. If a pipeline is used, the document must be selected for each processing resource used. Finally, right-click on ‘Run’ to run the application on the document or corpus.

See also the movie for loading and running processing resources.

For how to use the conditional versions of the pipelines see Section 3.7.1 and for saving/restoring the configuration of an application see Section 3.8.3.

3.7.1 Running PRs Conditionally on Document Features [#]

The ‘Conditional Pipeline’ and ‘Conditional Corpus Pipeline’ application types are conditional versions of the pipelines mentioned in Section 3.7 and allow processing resources to be run or not according to the value of a feature on the document. In terms of graphical interface, the only addition brought by the conditional versions of the applications is a box situated underneath the lists of available and selected resources which allows the user to choose whether the currently selected processing resource will run always, never or only on the documents that have a particular value for a named feature.

If the Yes option is selected then the corresponding resource will be run on all the documents processed by the application as in the case of non- conditional applications. If the No option is selected then the corresponding resource will never be run; the application will simply ignore its presence. This option can be used to temporarily and quickly disable an application component, for debugging purposes for example.

The If value of feature option permits running specific application components conditionally on document features. When selected, this option enables two text input fields that are used to enter the name of a feature and the value of that feature for which the corresponding processing resource will be run. When a conditional application is run over a document, for each component that has an associated condition, the value of the named feature is checked on the document and the component will only be used if the value entered by the user matches the one contained in the document features.

3.7.2 Doing Information Extraction with ANNIE [#]

This section describes how to load and run ANNIE (see Chapter 6) from GATE Developer. ANNIE is a good place to start because it provides a complete information extraction application, that you can run on any corpus. You can then view the effects.

From the File menu, select ‘Load ANNIE System’. To run it in its default state, choose ‘with Defaults’. This will automatically load all the ANNIE resources, and create a corpus pipeline called ANNIE with the correct resources selected in the right order, and the default input and output annotation sets.

If ‘without Defaults’ is selected, the same processing resources will be loaded, but a popup window will appear for each resource, which enables the user to specify a name and location for the resource. This is exactly the same procedure as for loading a processing resource individually, the difference being that the system automatically selects those resources contained within ANNIE. When the resources have been loaded, a corpus pipeline called ANNIE will be created as before.

The next step is to add a corpus (see Section 3.3), and select this corpus from the drop-down corpus menu in the Serial Application editor. Finally click on ‘Run’ from the Serial Application editor, or by right clicking on the application name in the resources pane and selecting ‘Run’. (Many people prefer to switch to the messages tab, then run their application by right-clicking on it in the resources pane, because then it is possible to monitor any messages that appear whilst the application is running.)

To view the results, double click on the filename in the left hand pane. No annotation sets nor annotations will be shown until annotations are selected in the annotation sets; the ‘Default’ set is indicated only with an unlabelled right-arrowhead which must be selected in order to make visible the available annotations. Open the default annotation set and select some of the annotations to see what the ANNIE application has done.

See also the movie for loading and running ANNIE.

3.7.3 Modifying ANNIE [#]

You will find the ANNIE resources in gate/plugins/ANNIE/resources. Simply locate the existing resources you want to modify, make a copy with a new name, edit them, and load the new resources into GATE as new Processing Resources (see Section 3.6).

3.8 Saving Applications and Language Resources [#]

In this section, we will describe how applications and language resources can be saved for use outside of GATE and for use with GATE at a later time. Section 3.8.1 talks about saving documents to file. Section 3.8.2 outlines how to use datastores. Section 3.8.3 talks about saving resource parameter states, and Section 3.8.4 talks about exporting applications.

3.8.1 Saving Documents to File [#]

There are three main ways to save annotated documents:

1. preserving the original markup, with optional added annotations;
2. in GATE’s own XML serialisation format (including all the annotations on the document);
3. by writing your own dump algorithm as a processing resource.

This section describes how to use the first two options.

Both types of data export are available in the popup menu triggered by right-clicking on a document in the resources tree (see Section 3.1): type 1 is called ‘Save Preserving Format’ and type 2 is called ‘Save as XML’.

Selecting the save as XML option leads to a file open dialogue; give the name of the file you want to create, and the whole document and all its data will be exported to that file. If you later create a document from that file, the state will be restored. (Note: because GATE’s annotation model is richer than that of XML, and because our XML dump implementation sometimes cuts corners², the state may not be identical after restoration. If your intention is to store the state for later use, use a DataStore instead.)

The ‘Save Preserving Format’ option also leads to a file dialogue; give a name and the data you require will be dumped into the file. The action can be used for documents that were created from files using the XML or HTML format. It will save all the original tags as well as the document annotations that are currently displayed in the ‘Annotations List’ view. This option is useful for selectively saving only some annotation types.

The annotations are saved as normal document tags, using the annotation type as the tag name. If the advanced option ‘Include annotation features for “Save Preserving Format”’ (see Section 2.4) is set to true, then the annotation features will also be saved as tag attributes.

Using this operation for GATE documents that were not created from an HTML or XML file results in a plain text file, with in-line tags for the saved annotations.

Note that GATE’s model of annotation allows graph structures, which are difficult to represent in XML (XML is a tree-structured representation format). During the dump process, annotations that cross each other in ways that cannot be represented in legal XML will be discarded, and a warning message printed.

3.8.2 Saving and Restoring LRs in Datastores [#]

Where corpora are large, the memory available may not be sufficient to have all documents open simultaneously. The datastore functionality provides the option to save documents to disk and open them only one at a time for processing. This means that much larger corpora can be used. A datastore can also be useful for saving documents in an efficient and lossless way.

To save a text in a datastore, a new datastore must first be created if one does not already exist. Create a datastore by right clicking on Datastore in the left hand pane, and select the option ‘Create Datastore’. Select the data store type you wish to use. Create a directory to be used as the datastore (note that the datastore is a directory and not a file).

You can either save a whole corpus to the datastore (in which case the structure of the corpus will be preserved) or you can save individual documents. The recommended method is to save the whole corpus. To save a corpus, right click on the corpus name and select the ‘Save to...’ option (giving the name of the datastore created earlier). To save individual documents to the datastore, right clicking on each document name and follow the same procedure.

To load a document from a datastore, do not try to load it as a language resource. Instead, open the datastore by right clicking on Datastore in the left hand pane, select ‘Open Datastore’ and choose the datastore to open. The datastore tree will appear in the main window. Double click on a corpus or document in this tree to open it. To save a corpus and document back to the same datastore, simply select the ‘Save’ option.

See also the movie for creating a datastore and the movie for loading corpus and documents from a datastore.

3.8.3 Saving Resource Parameter States to File [#]

Resources, and applications that are made up of them, are created based on the settings of their parameters (see Section 3.6). It is possible to save the data used to create an application to a file and re-load it later. To save the application to a file, right click on it in the resources tree and select ‘Save application state’, which will give you a file creation dialogue.

To restore the application later, select ‘Restore application from file’ from the ‘File’ menu.

Note that the data that is saved represents how to recreate an application – not the resources that make up the application itself. So, for example, if your application has a resource that initialises itself from some file (e.g. a grammar, a document) then that file must still exist when you restore the application.

In case you don’t want to save the corpus configuration associated with the application then you must select ‘<none>’ in the corpus list of the application before saving the application.

The file resulting from saving the application state contains the values of the initialisation parameters for all the processing resources contained by the stored application. For the parameters of type URL (which are typically used to select external resources such as grammars or rules files) a transformation is applied so that all the paths are relative to the location of the file used to store the state. This means that the resource files used by an application do not need to be in the same location as when the application was initially created but rather in the same location relative to the location of the application file. This allows the creation and deployment of portable applications by keeping the application file and the resource files used by the application together.

If you want to save your application along with all the resources it requires you can use the ‘Export for Teamware’ option (see Section 3.8.4).

See also the movie for saving and restoring applications.

3.8.4 Saving an Application with its Resources (e.g. GATE Teamware) [#]

When you save an application using the ‘Save application state’ option (see Section 3.8.3), the saved file contains references to the plugins that were loaded when the application was saved, and to any resource files required by the application. To be able to reload the file, these plugins and other dependencies must exist at the same locations (relative to the saved state file). While this is fine for saving and loading applications on a single machine it means that if you want to package your application to run it elsewhere (e.g. deploy it to a GATE Teamware installation) then you need to be careful to include all the resource files and plugins at the right locations in your package. The ‘Export for Teamware’ option on the right-click menu for an application helps to automate this process.

When you export an application in this way, GATE Developer produces a ZIP file containing the saved application state (in the same format as ‘Save application state’). Any plugins and resource files that the application refers to are also included in the zip file, and the relative paths in the saved state are rewritten to point to the correct locations within the package. The resulting package is therefore self-contained and can be copied to another machine and unpacked there, or passed to your Teamware Administrator for deployment.

As well as selecting the location where you want to save the package, the ‘Export for Teamware’ option will also prompt you to select the annotation sets that your application uses for input and output. For example, if your application makes use of the unpacked XML markup in source documents and creates annotations in the default set then you would select ‘Original markups’ as an input set and the ‘<Default annotation set>’ as an output set. GATE Developer will try to make an educated guess at the correct sets but you should check and amend the lists as necessary.

There are a few important points to note about the export process:

• The complete contents of all the plugin directories that are loaded when you perform the export will be included in the resulting package. Use the plugin manager to unload any plugins your application is not using before you export it.
• If your application refers to a resource file in a directory that is not under one of the loaded plugins, the entire contents of this directory will be recursively included in the package. If you have a number of unrelated resources in a single directory (e.g. many sets of large gazetteer lists) you may want to separate them into separate directories so that only the relevant ones are included in the package.
• The packager only knows about resources that your application refers to directly in its parameters. For example, if your application includes a multi-phase JAPE grammar the packager will only consider the main grammar file, not any of its sub-phases. If the sub-phases are not contained in the same directory as the main grammar you may find they are not included. If indirect references of this kind are all to files under the same directory as the ‘master’ file it will work OK.

If you require more flexibility than this option provides you should read Section E.2, which describes the underlying Ant task that the exporter uses.

3.9 Keyboard Shortcuts [#]

You can use various keyboard shortcuts for common tasks in GATE Developer. These are listed in this section.

General (Section 3.1):

• F1 Display a help page for the selected component
• Alt+F4 Exit the application without confirmation
• Tab Put the focus on the next component or frame
• Shift+Tab Put the focus on the previous component or frame
• F6 Put the focus on the next frame
• Shift+F6 Put the focus on the previous frame
• Alt+F Show the File menu
• Alt+O Show the Options menu
• Alt+T Show the Tools menu
• Alt+H Show the Help menu
• F10 Show the first menu

Resources tree (Section 3.1):

• Enter Show the selected resources
• Ctrl+H Hide the selected resource
• Ctrl+Shift+H Hide all the resources
• F2 Rename the selected resource
• Ctrl+F4 Close the selected resource

Document editor (Section 3.2):

• Ctrl+F Show the search dialog for the document
• Ctrl+S Save the document in a file
• F3 Show/Hide the annotation sets
• Shift+F3 Show the annotation sets with preselection
• F4 Show/Hide the annotations list
• F5 Show/Hide the coreference editor
• F7 Show/Hide the text

Annotation editor (Section 3.4):

• Right/Left Grow/Shrink the annotation span at its start
• Alt+Right/Alt+Left Grow/Shrink the annotation span at its end
• +Shift/+Ctrl+Shift Use a span increment of 5/10 characters
• Alt+Delete Delete the currently edited annotation

Annic/Lucene datastore (Chapter 9):

• Alt+Enter Search the expression in the datastore
• Alt+Backspace Delete the search expression
• Alt+Right Display the next page of results
• Alt+Left Display the row manager
• Alt+E Export the results to a file

Annic/Lucene query text field (Chapter 9):

• Ctrl+Enter Insert a new line
• Enter Search the expression
• Alt+Top Select the previous result
• Alt+Bottom Select the next result

3.10 Miscellaneous [#]

3.10.1 Stopping GATE from Restoring Developer Sessions/Options [#]

GATE can remember Developer options and the state of the resource tree when it exits. The options are saved by default; the session state is not saved by default. This default behaviour can be changed from the ‘Advanced’ tab of the ‘Configuration’ choice on the ‘Options’ menu.

If a problem occurs and the saved data prevents GATE Developer from starting, you can fix this by deleting the configuration and session data files. These are stored in your home directory, and are called gate.xml and gate.sesssion or .gate.xml and .gate.sesssion depending on platform. On Windows your home is:

95, 98, NT:

Windows Directory/profiles/username

2000, XP:

Windows Drive/Documents and Settings/username

3.10.2 Working with Unicode [#]

GATE provides various facilities for working with Unicode beyond those that come as default with Java³:

1. a Unicode editor with input methods for many languages;
2. use of the input methods in all places where text is edited in the GUI;
3. a development kit for implementing input methods;
4. ability to read diverse character encodings.

1 using the editor:
In GATE Developer, select ‘Unicode editor’ from the ‘Tools’ menu. This will display an editor window, and, when a language with a custom input method is selected for input (see next section), a virtual keyboard window with the characters of the language assigned to the keys on the keyboard. You can enter data either by typing as normal, or with mouse clicks on the virtual keyboard.

2 configuring input methods:
In the editor and in GATE Developer’s main window, the ‘Options’ menu has an ‘Input methods’ choice. All supported input languages (a superset of the JDK languages) are available here. Note that you need to use a font capable of displaying the language you select. By default GATE Developer will choose a Unicode font if it can find one on the platform you’re running on. Otherwise, select a font manually from the ‘Options’ menu ‘Configuration’ choice.

3 using the development kit:
GUK, the GATE Unicode Kit, is documented at http://gate.ac.uk/gate/doc/javadoc/guk/package-summary.html.

4 reading different character encodings:
When you create a document from a URL pointing to textual data in GATE, you have to tell the system what character encoding the text is stored in. By default, GATE will set this parameter to be the empty string. This tells Java to use the default encoding for whatever platform it is running on at the time – e.g. on Western versions of Windows this will be ISO-8859-1, and Eastern ones ISO-8859-9. A popular way to store Unicode documents is in UTF-8, which is a superset of ASCII (but can still store all Unicode data); if you get an error message about document I/O during reading, try setting the encoding to UTF-8, or some other locally popular encoding. (To see a list of available encodings, try opening a document in GATE’s unicode editor – you will be prompted to select an encoding.)

Chapter 4
CREOLE: the GATE Component Model [#]

The GATE architecture is based on components: reusable chunks of software with well-defined interfaces that may be deployed in a variety of contexts. The design of GATE is based on an analysis of previous work on infrastructure for LE, and of the typical types of software entities found in the fields of NLP and CL (see in particular chapters 4–6 of [Cunningham 00]). Our research suggested that a profitable way to support LE software development was an architecture that breaks down such programs into components of various types. Because LE practice varies very widely (it is, after all, predominantly a research field), the architecture must avoid restricting the sorts of components that developers can plug into the infrastructure. The GATE framework accomplishes this via an adapted version of the Java Beans component framework from Sun, as described in section 4.2.

GATE components may be implemented by a variety of programming languages and databases, but in each case they are represented to the system as a Java class. This class may do nothing other than call the underlying program, or provide an access layer to a database; on the other hand it may implement the whole component.

GATE components are one of three types:

• LanguageResources (LRs) represent entities such as lexicons, corpora or ontologies;
• ProcessingResources (PRs) represent entities that are primarily algorithmic, such as parsers, generators or ngram modellers;
• VisualResources (VRs) represent visualisation and editing components that participate in GUIs.

The distinction between language resources and processing resources is explored more fully in section C.1.1. Collectively, the set of resources integrated with GATE is known as CREOLE: a Collection of REusable Objects for Language Engineering.

In the rest of this chapter:

• Section 4.3 describes the lifecycle of GATE components;
• Section 4.4 describes how Processing Resources can be grouped into applications;
• Section 4.5 describes the relationship between Language Resources and their datastores;
• Section 4.6 summarises GATE’s set of built-in components;
• Section 4.7 describes how configuration data for Resource types is supplied to GATE.

4.1 The Web and CREOLE [#]

GATE allows resource implementations and Language Resource persistent data to be distributed over the Web, and uses Java annotations and XML for configuration of resources (and GATE itself).

Resource implementations are grouped together as ‘plugins’, stored at a URL (when the resources are in the local file system this can be a file:/ URL). When a plugin is loaded into GATE it looks for a configuration file called creole.xml relative to the plugin URL and uses the contents of this file to determine what resources this plugin declares and where to find the classes that implement the resource types (typically these classes are stored in a JAR file in the plugin directory). Configuration data for the resources may be stored directly in the creole.xml file, or it may be stored as Java annotations on the resource classes themselves; in either case GATE retrieves this configuration information and adds the resource definitions to the CREOLE register. When a user requests an instantiation of a resource, GATE creates an instance of the resource class in the virtual machine.

Language resource data can be stored in binary serialised form in the local file system.

4.2 The GATE Framework [#]

We can think of the GATE framework as a backplane into which users can plug CREOLE components. The user gives the system a list of URLs to search when it starts up, and components at those locations are loaded by the system.

The backplane performs these functions:

• component discovery, bootstrapping, loading and reloading;
• management and visualisation of native data structures for common information types;
• generalised data storage and process execution.

A set of components plus the framework is a deployment unit which can be embedded in another application.

At their most basic, all GATE resources are Java Beans, the Java platform’s model of software components. Beans are simply Java classes that obey certain interface conventions:

• beans must have no-argument constructors.
• beans have properties, defined by pairs of methods named by the convention setProp and getProp .

GATE uses Java Beans conventions to construct and configure resources at runtime, and defines interfaces that different component types must implement.

4.3 The Lifecycle of a CREOLE Resource [#]

CREOLE resources exhibit a variety of forms depending on the perspective they are viewed from. Their implementation is as a Java class plus an XML metadata file living at the same URL. When using GATE Developer, resources can be loaded and viewed via the resources tree (left pane) and the ‘create resource’ mechanism. When programming with GATE Embedded, they are Java objects that are obtained by making calls to GATE’s Factory class. These various incarnations are the phases of a CREOLE resource’s ‘lifecycle’. Depending on what sort of task you are using GATE for, you may use resources in any or all of these phases. For example, you may only be interested in getting a graphical view of what GATE’s ANNIE Information Extraction system (see Chapter 6) does; in this case you will use GATE Developer to load the ANNIE resources, and load a document, and create an ANNIE application and run it on the document. If, on the other hand, you want to create your own resources, or modify the Java code of an existing resource (as opposed to just modifying its grammar, for example), you will need to deal with all the lifecycle phases.

The various phases may be summarised as:

Creating a new resource from scratch (bootstrapping).

To create the binary image of a resource (a Java class in a JAR file), and the XML file that describes the resource to GATE, you need to create the appropriate .java file(s), compile them and package them as a .jar. GATE provides a bootstrap tool to start this process – see Section 7.10. Alternatively you can simply copy code from an existing resource.

Instantiating a resource in GATE Embedded.

To create a resource in your own Java code, use GATE’s Factory class (this takes care of parameterising the resource, restoring it from a database where appropriate, etc. etc.). Section 7.2 describes how to do this.

Loading a resource into GATE Developer.

To load a resource into GATE Developer, use the various ‘New ... resource’ options from the File menu and elsewhere. See Section 3.1.

Resource configuration and implementation.

GATE’s bootstrap tool will create an empty resource that does nothing. In order to achieve the behaviour you require, you’ll need to change the configuration of the resource (by editing the creole.xml file) and/or change the Java code that implements the resource. See section 4.7.

4.4 Processing Resources and Applications [#]

PRs can be combined into applications. Applications model a control strategy for the execution of PRs. In GATE, applications are called ‘controllers’ accordingly.

Currently only sequential, or pipeline, execution is supported. There are two main types of pipeline:

Simple pipelines

simply group a set of PRs together in order and execute them in turn. The implementing class is called SerialController.

Corpus pipelines

are specific for LanguageAnalysers – PRs that are applied to documents and corpora. A corpus pipeline opens each document in the corpus in turn, sets that document as a runtime parameter on each PR, runs all the PRs on the corpus, then closes the document. The implementing class is called SerialAnalyserController.

Conditional versions of these controllers are also available. These allow processing resources to be run conditionally on document features. See Section 3.7.1 for how to use these.

Controllers are themselves PRs – in particular a simple pipeline is a standard PR and a corpus pipeline is a LanguageAnalyser – so one pipeline can be nested in another. This is particularly useful with conditional controllers to group together a set of PRs that can all be turned on or off as a group.

There is also a real-time version of the corpus pipeline. When creating such a controller, a timeout parameter needs to be set which determines the maximum amount of time (in milliseconds) allowed for the processing of a document. Documents that take longer to process, are simply ignored and the execution moves to the next document after the timeout interval has lapsed.

All controllers have special handling for processing resources that implement the interface gate.creole.ControllerAwarePR. This interface provides methods that are called by the controller at the start and end of the whole application’s execution – for a corpus pipeline, this means before any document has been processed and after all documents in the corpus have been processed, which is useful for PRs that need to share data structures across the whole corpus, build aggregate statistics, etc. For full details, see the JavaDoc documentation for ControllerAwarePR.

4.5 Language Resources and Datastores [#]

Language Resources can be stored in Datastores. Datastores are an abstract model of disk-based persistence, which can be implemented by various types of storage mechanism. Here are the types implemented:

Serial Datastores

are based on Java’s serialisation system, and store data directly into files and directories.

Lucene Datastores

is a full-featured annotation indexing and retrieval system. It is provided as part of an extension of the Serial Datastores. See Section 9 for more details.

4.6 Built-in CREOLE Resources [#]

GATE comes with various built-in components:

• Language Resources modelling Documents and Corpora, and various types of Annotation Schema – see Chapter 5.
• Processing Resources that are part of the ANNIE system – see Chapter 6.
• Gazetteers – see Chapter 13.
• Ontologies – see Chapter 14.
• Machine Learning resources – see Chapter 15.
• Alignment tools – see Chapter 16.
• Parsers and taggers – see Chapter 17.
• Other miscellaneous resources – see Chapter 19.

4.7 CREOLE Resource Configuration [#]

This section describes how to supply GATE with the configuration data it needs about a resource, such as what its parameters are, how to display it if it has a visualisation, etc. Several GATE resources can be grouped into a single plugin, which is a directory containing an XML configuration file called creole.xml. Configuration data for the plugin’s resources can be given in the creole.xml file or directly in the Java source file using Java 5 annotations.

A creole.xml file has a root element <CREOLE-DIRECTORY>, but the further contents of this element depend on the configuration style. The following three sections discuss the different styles – all-XML, all-annotations and a mixture of the two.

4.7.1 Configuration with XML [#]

To configure your resources in the creole.xml file, the <CREOLE-DIRECTORY> element should contain one <RESOURCE> element for each resource type in the plugin. The <RESOURCE> elements may optionally be contained within a <CREOLE> element (to allow a single creole.xml file to be built up by concatenating multiple separate files). For example:

Basic Resource-Level Data

Each resource must give a name, a Java class and the JAR file that it can be loaded from. The above example is taken from the Parser_Minipar plugin, and defines a single resource with a number of parameters.

The full list of valid elements under <RESOURCE> is as follows:

NAME

the name of the resource, as it will appear in the ‘New’ menu in GATE Developer. If omitted, defaults to the bare name of the resource class (without a package name).

CLASS

the fully qualified name of the Java class that implements this resource.

JAR

names JAR files required by this resource (paths are relative to the location of creole.xml). Typically this will be the JAR file containing the class named by the <CLASS> element, but additional <JAR> elements can be used to name third-party JAR files that the resource depends on.

COMMENT

a descriptive comment about the resource, which will appear as the tooltip when hovering over an instance of this resource in the resources tree in GATE Developer. If omitted, no comment is used.

HELPURL

a URL to a help document on the web for this resource. It is used in the help browser inside GATE Developer.

INTERFACE

the interface type implemented by this resource, for example new types of document would specify <INTERFACE>gate.Document</INTERFACE>.

ICON

the icon used to represent this resource in GATE Developer. This is a path inside the plugin’s JAR file, for example <ICON>/some/package/icon.png</ICON>. If the path specified does not start with a forward slash, it is assumed to name an icon from the GATE default set, which is located in gate.jar at gate/resources/img. If no icon is specified, a generic language resource or processing resource icon (as appropriate) is used.

PRIVATE

if present, this resource type is hidden in the GATE Developer GUI, i.e. it is not shown in the ‘New’ menus. This is useful for resource types that are intended to be created internally by other resources, or for resources that have parameters of a type that cannot be set in the GUI. <PRIVATE/> resources can still be created in Java code using the Factory.

AUTOINSTANCE (and HIDDEN-AUTOINSTANCE)

tells GATE to automatically create instances of this resource when the plugin is loaded. Any number of auto instances may be defined, GATE will create them all. Each <AUTOINSTANCE> element may optionally contain <PARAM NAME="..." VALUE="..." /> elements giving parameter values to use when creating the instance. Any parameters not specified explicitly will take their default values. Use <HIDDEN-AUTOINSTANCE> if you want the auto instances not to show up in GATE Developer – this is useful for things like document formats where there should only ever be a single instance in GATE and that instance should not be deleted.

For visual resources, a <GUI> element should also be provided. This takes a TYPE attribute, which can have the value LARGE or SMALL. LARGE means that the visual resource is a large viewer and should appear in the main part of the GATE Developer window on the right hand side, SMALL means the VR is a small viewer which appears in the space below the resources tree in the bottom left. The <GUI> element supports the following sub-elements:

RESOURCE_DISPLAYED

the type of GATE resource this VR can display. Any resource whose type is assignable to this type will be displayed with this viewer, so for example a VR that can display all types of document would specify gate.Document, whereas a VR that can only display the default GATE document implementation would specify gate.corpora.DocumentImpl.

MAIN_VIEWER

if present, GATE will consider this VR to be the ‘most important’ viewer for the given resource type, and will ensure that if several different viewers are all applicable to this resource, this viewer will be the one that is initially visible.

For annotation viewers, you should specify an <ANNOTATION_TYPE_DISPLAYED> element giving the annotation type that the viewer can display (e.g. Sentence).

Resource Parameters

Resources may also have parameters of various types. These resources, from the GATE distribution, illustrate the various types of parameters:

Parameters may be optional, and may have default values (and may have comments to describe their purpose, which is displayed by GATE Developer during interactive parameter setting).

Some PR parameters are execution time (RUNTIME), some are initialisation time. E.g. at execution time a doc is supplied to a language analyser; at initialisation time a grammar may be supplied to a language analyser.

The <PARAMETER> tag takes the following attributes:

NAME:

name of the JavaBean property that the parameter refers to, i.e. for a parameter named ‘someParam’ the class must have setSomeParam and getSomeParam methods.¹

DEFAULT:

default value (see below).

RUNTIME:

doesn’t need setting at initialisation time, but must be set before calling execute(). Only meaningful for PRs

OPTIONAL:

not required

COMMENT:

for display purposes

ITEM_CLASS_NAME:

(only applies to parameters whose type is java.util.Collection or a type that implements or extends this) this specifies the type of elements the collection contains, so GATE can use the right type when parameters are set. If omitted, GATE will pass in the elements as Strings.

SUFFIXES:

(only applies to parameters of type java.net.URL) a semicolon-separated list of file suffixes that this parameter typically accepts, used as a filter in the file chooser provided by GATE Developer to select a local file as the parameter value.

It is possible for two or more parameters to be mutually exclusive (i.e. a user must specify one or the other but not both). In this case the <PARAMETER> elements should be grouped together under an <OR> element.

The type of the parameter is specified as the text of the <PARAMETER> element, and the type supplied must match the return type of the parameter’s get method. Any reference type (class, interface or enum) may be used as the parameter type, including other resource types – in this case GATE Developer will offer a list of the loaded instances of that resource as options for the parameter value. Primitive types (char, boolean, …) are not supported, instead you should use the corresponding wrapper type (java.lang.Character, java.lang.Boolean, …). If the getter returns a parameterized type (e.g. List<Integer>) you should just specify the raw type (java.util.List) here².

The DEFAULT string is converted to the appropriate type for the parameter - java.lang.String parameters use the value directly, primitive wrapper types e.g. java.lang.Integer use their respective valueOf methods, and other built-in Java types can have defaults specified provided they have a constructor taking a String.

The type java.net.URL is treated specially: if the default string is not an absolute URL (e.g. http://gate.ac.uk/) then it is treated as a path relative to the location of the creole.xml file. Thus a DEFAULT of ‘resources/main.jape’ in the file file:/opt/MyPlugin/creole.xml is treated as the absolute URL file:/opt/MyPlugin/resources/main.jape.

For Collection-valued parameters multiple values may be specified, separated by semicolons, e.g. ‘foo;bar;baz’; if the parameter’s type is an interface – Collection or one of its sub-interfaces (e.g. List) – a suitable concrete class (e.g. ArrayList, HashSet) will be chosen automatically for the default value.

For parameters of type gate.FeatureMap multiple name=value pairs can be specified, e.g. ‘kind=word;orth=upperInitial’. For enum-valued parameters the default string is taken as the name of the enum constant to use. Finally, if no DEFAULT attribute is specified, the default value is null.

4.7.2 Configuring Resources using Annotations [#]

As an alternative to the XML configuration style, GATE provides Java 5 annotation types to embed the configuration data directly in the Java source code. @CreoleResource is used to mark a class as a GATE resource, and parameter information is provided through annotations on the JavaBean set methods. At runtime these annotations are read and mapped into the equivalent entries in creole.xml before parsing. The metadata annotation types are all marked @Documented so the CREOLE configuration data will be visible in the generated JavaDoc documentation.

For more detailed information, see the JavaDoc documentation for gate.creole.metadata.

To use annotation-driven configuration a creole.xml file is still required but it need only contain the following:

This tells GATE to load myPlugin.jar and scan its contents looking for resource classes annotated with @CreoleResource. Other JAR files required by the plugin can be specified using other <JAR> elements without SCAN="true".

Basic Resource-Level Data

To mark a class as a CREOLE resource, simply use the @CreoleResource annotation (in the gate.creole.metadata package), for example:

The @CreoleResource annotation provides slots for all the values that can be specified under <RESOURCE> in creole.xml, except <CLASS> (inferred from the name of the annotated class) and <JAR> (taken to be the JAR containing the class):

name

(String) the name of the resource, as it will appear in the ‘New’ menu in GATE Developer. If omitted, defaults to the bare name of the resource class (without a package name). (XML equivalent <NAME>)

comment

(String) a descriptive comment about the resource, which will appear as the tooltip when hovering over an instance of this resource in the resources tree in GATE Developer. If omitted, no comment is used. (XML equivalent <COMMENT>)

helpURL

(String) a URL to a help document on the web for this resource. It is used in the help browser inside GATE Developer. (XML equivalent <HELPURL>)

isPrivate

(boolean) should this resource type be hidden from the GATE Developer GUI, so it does not appear in the ‘New’ menus? If omitted, defaults to false (i.e. not hidden). (XML equivalent <PRIVATE/>)

icon

(String) the icon to use to represent the resource in GATE Developer. If omitted, a generic language resource or processing resource icon is used. (XML equivalent <ICON>, see the description above for details)

interfaceName

(String) the interface type implemented by this resource, for example a new type of document would specify "gate.Document" here. (XML equivalent <INTERFACE>)

autoInstances

(array of @AutoInstance annotations) definitions for any instances of this resource that should be created automatically when the plugin is loaded. If omitted, no auto-instances are created by default. (XML equivalent, one or more <AUTOINSTANCE> and/or <HIDDEN-AUTOINSTANCE> elements, see the description above for details)

For visual resources only, the following elements are also available:

guiType

(GuiType enum) the type of GUI this resource defines. (XML equivalent <GUI TYPE="LARGE|SMALL">)

resourceDisplayed

(String) the class name of the resource type that this VR displays, e.g. "gate.Corpus". (XML equivalent <RESOURCE_DISPLAYED>)

mainViewer

(boolean) is this VR the ‘most important’ viewer for its displayed resource type? (XML equivalent <MAIN_VIEWER/>, see above for details)

For annotation viewers, you should specify an annotationTypeDisplayed element giving the annotation type that the viewer can display (e.g. Sentence).

Resource Parameters

Parameters are declared by placing annotations on their JavaBean set methods. To mark a setter method as a parameter, use the @CreoleParameter annotation, for example:

GATE will infer the parameter’s name from the name of the JavaBean property in the usual way (i.e. strip off the leading set and convert the following character to lower case, so in this example the name is abbrListUrl). The parameter name is not taken from the name of the method parameter. The parameter’s type is inferred from the type of the method parameter (java.net.URL in this case).

The annotation elements of @CreoleParameter correspond to the attributes of the <PARAMETER> tag in the XML configuration style:

comment

(String) an optional descriptive comment about the parameter. (XML equivalent COMMENT)

defaultValue

(String) the optional default value for this parameter. The value is specified as a string but is converted to the relevant type by GATE according to the conversions described in the previous section. Note that relative path default values for URL-valued parameters are still relative to the location of the creole.xml file, not the annotated class. (XML equivalent DEFAULT)

suffixes

(String) for URL-valued parameters, a semicolon-separated list of default file suffixes that this parameter accepts. (XML equivalent SUFFIXES)

collectionElementType

(Class) for Collection-valued parameters, the type of the elements in the collection. This can usually be inferred from the generic type information, for example public void setIndices(List<Integer> indices), but must be specified if the set method’s parameter has a raw (non-parameterized) type. (XML equivalent ITEM_CLASS_NAME)

Mutually-exclusive parameters (such as would be grouped in an <OR> in creole.xml) are handled by adding a disjunction="label" to the @CreoleParameter annotation – all parameters that share the same label are grouped in the same disjunction.

Optional and runtime parameters are marked using extra annotations, for example:

Inheritance

Unlike with pure XML configuration, when using annotations a resource will inherit any configuration data that was not explicitly specified from annotations on its parent class and on any interfaces it implements. Specifically, if you do not specify a comment, interfaceName, icon, annotationTypeDisplayed or the GUI-related elements (guiType and resourceDisplayed) on your @CreoleResource annotation then GATE will look up the class tree for other @CreoleResource annotations, first on the superclass, its superclass, etc., then at any implemented interfaces, and use the first value it finds. This is useful if you are defining a family of related resources that inherit from a common base class.

The resource name and the isPrivate and mainViewer flags are not inherited.

Parameter definitions are inherited in a similar way. This is one of the big advantages of annotation configuration over pure XML – if one resource class extends another then with pure XML configuration all the parent class’s parameter definitions must be duplicated in the subclass’s creole.xml definition. With annotations, parameters are inherited from the parent class (and its parent, etc.) as well as from any interfaces implemented. For example, the gate.LanguageAnalyser interface provides two parameter definitions via annotated set methods, for the corpus and document parameters. Any @CreoleResource annotated class that implements LanguageAnalyser, directly or indirectly, will get these parameters automatically.

Of course, there are some cases where this behaviour is not desirable, for example if a subclass calculates a value for a superclass parameter rather than having the user set it directly. In this case you can hide the parameter by overriding the set method in the subclass and using a marker annotation:

The overriding method will typically just call the superclass one, as its only purpose is to provide a place to put the @HiddenCreoleParameter annotation.

Alternatively, you may want to override some of the configuration for a parameter but inherit the rest from the superclass. Again, this is handled by trivially overriding the set method and re-annotating it:

Note that for backwards compatibility, data is only inherited from superclass annotations if the subclass is itself annotated with @CreoleResource. If the subclass is not annotated then GATE assumes that all its configuration is contained in creole.xml in the usual way.

4.7.3 Mixing the Configuration Styles [#]

It is possible and often useful to mix and match the XML and annotation-driven configuration styles. The rule is always that anything specified in the XML takes priority over the annotations. The following examples show what this allows.

Overriding Configuration for a Third-Party Resource

Suppose you have a plugin from some third party that uses annotation-driven configuration. You don’t have the source code but you would like to override the default value for one of the parameters of one of the plugin’s resources. You can do this in the creole.xml:

The default value for the listUrl parameter in the annotated class will be replaced by your value.

External AUTOINSTANCEs

For resources like document formats, where there should always and only be one instance in GATE at any time, it makes sense to put the auto-instance definitions in the @CreoleResource annotation. But if the automatically created instances are a convenience rather than a necessity it may be better to define them in XML so other users can disable them without re-compiling the class:

Inheriting Parameters

If you would prefer to use XML configuration for your own resources, but would like to benefit from the parameter inheritance features of the annotation-driven approach, you can write a normal creole.xml file with all your configuration and just add a blank @CreoleResource annotation to your class. For example:

N.B. Without the @CreoleResource the parameters would not be inherited.

Chapter 5
Language Resources: Corpora, Documents and Annotations [#]

This chapter documents GATE’s model of corpora, documents and annotations on documents. Section 5.1 describes the simple attribute/value data model that corpora, documents and annotations all share. Section 5.2, Section 5.3 and Section 5.4 describe corpora, documents and annotations on documents respectively. Section 5.5 describes GATE’s support for diverse document formats, and Section 5.5.2 describes facilities for XML input/output.

5.1 Features: Simple Attribute/Value Data [#]

GATE has a single model for information that describes documents, collections of documents (corpora), and annotations on documents, based on attribute/value pairs. Attribute names are strings; values can be any Java object. The API for accessing this feature data is Java’s Map interface (part of the Collections API).

5.2 Corpora: Sets of Documents plus Features [#]

A Corpus in GATE is a Java Set whose members are Documents. Both Corpora and Documents are types of LanguageResource (LR); all LRs have a FeatureMap (a Java Map) associated with them that stored attribute/value information about the resource. FeatureMaps are also used to associate arbitrary information with ranges of documents (e.g. pieces of text) via the annotation model (see below).

Documents have a DocumentContent which is a text at present (future versions may add support for audiovisual content) and one or more AnnotationSets which are Java Sets.

5.3 Documents: Content plus Annotations plus Features [#]

Documents are modelled as content plus annotations (see Section 5.4) plus features (see Section 5.1). The content of a document can be any subclass of DocumentContent.

5.4 Annotations: Directed Acyclic Graphs [#]

Annotations are organised in graphs, which are modelled as Java sets of Annotation. Annotations may be considered as the arcs in the graph; they have a start Node and an end Node, an ID, a type and a FeatureMap. Nodes have pointers into the sources document, e.g. character offsets.

5.4.1 Annotation Schemas [#]

Annotation schemas provide a means to define types of annotations in GATE. GATE uses the XML Schema language supported by W3C for these definitions. When using GATE Developer to create/edit annotations, a component is available (gate.gui.SchemaAnnotationEditor) which is driven by an annotation schema file. This component will constrain the data entry process to ensure that only annotations that correspond to a particular schema are created. (Another component allows unrestricted annotations to be created.)

Schemas are resources just like other GATE components. Below we give some examples of such schemas. Section 3.4.6 describes how to create new schemas.

Date Schema

Person Schema

Address Schema

5.4.2 Examples of Annotated Documents [#]

This section shows some simple examples of annotated documents.

This material is adapted from [Grishman 97], the TIPSTER Architecture Design document upon which GATE version 1 was based. Version 2 has a similar model, although annotations are now graphs, and instead of multiple spans per annotation each annotation now has a single start/end node pair. The current model is largely compatible with [Bird & Liberman 99], and roughly isomorphic with "stand-off markup" as latterly adopted by the SGML/XML community.

Each example is shown in the form of a table. At the top of the table is the document being annotated; immediately below the line with the document is a ruler showing the position (byte offset) of each character (see TIPSTER Architecture Design Document).

Underneath this appear the annotations, one annotation per line. For each annotation is shown its Id, Type, Span (start/end offsets derived from the start/end nodes), and Features. Integers are used as the annotation Ids. The features are shown in the form name = value.

The first example shows a single sentence and the result of three annotation procedures: tokenization with part-of-speech assignment, name recognition, and sentence boundary recognition. Each token has a single feature, its part of speech (pos), using the tag set from the University of Pennsylvania Tree Bank; each name also has a single feature, indicating the type of name: person, company, etc.

Annotations will typically be organized to describe a hierarchical decomposition of a text. A simple illustration would be the decomposition of a sentence into tokens. A more complex case would be a full syntactic analysis, in which a sentence is decomposed into a noun phrase and a verb phrase, a verb phrase into a verb and its complement, etc. down to the level of individual tokens. Such decompositions can be represented by annotations on nested sets of spans. Both of these are illustrated in the second example, which is an elaboration of our first example to include parse information. Each non-terminal node in the parse tree is represented by an annotation of type parse.

In most cases, the hierarchical structure could be recovered from the spans. However, it may be desirable to record this structure directly through a constituents feature whose value is a sequence of annotations representing the immediate constituents of the initial annotation. For the annotations of type parse, the constituents are either non-terminals (other annotations in the parse group) or tokens. For the sentence annotation, the constituents feature points to the constituent tokens. A reference to another annotation is represented in the table as "[ Annotation Id]"; for example, "[3]" represents a reference to annotation 3. Where the value of an feature is a sequence of items, these items are separated by commas. No special operations are provided in the current architecture for manipulating constituents. At a less esoteric level, annotations can be used to record the overall structure of documents, including in particular documents which have structured headers, as is shown in the third example (Table 5.3).

If the Addressee, Source, ... annotations are recorded when the document is indexed for retrieval, it will be possible to perform retrieval selectively on information in particular fields. Our final example (Table 5.4) involves an annotation which effectively modifies the document. The current architecture does not make any specific provision for the modification of the original text. However, some allowance must be made for processes such as spelling correction. This information will be recorded as a correction feature on token annotations and possibly on name annotations:

5.4.3 Creating, Viewing and Editing Diverse Annotation Types [#]

Note that annotation types should consist of a single word with no spaces. Otherwise they may not be recognised by other components such as JAPE transducers, and may create problems when annotations are saved as inline (‘Save Preserving Format’ in the context menu).

To view and edit annotation types, see Section 3.4. To add annotations of a new type, see Section 3.4.5. To add a new annotation schema, see Section 3.4.6.

5.5 Document Formats [#]

The following document formats are supported by GATE:

• Plain Text
• HTML
• SGML
• XML
• RTF
• Email
• PDF (some documents)
• Microsoft Word (some documents)

By default GATE will try and identify the type of the document, then strip and convert any markup into GATE’s annotation format. To disable this process, set the markupAware parameter on the document to false.

When reading a document of one of these types, GATE extracts the text between tags (where such exist) and create a GATE annotation filled as follows:

The name of the tag will constitute the annotation’s type, all the tags attributes will materialize in the annotation’s features and the annotation will span over the text covered by the tag. A few exceptions of this rule apply for the RTF, Email and Plain Text formats, which will be described later in the input section of these formats.

The text between tags is extracted and appended to the GATE document’s content and all annotations created from tags will be placed into a GATE annotation set named ‘Original markups’.

Example:

If the markup is like this:

then the annotation created by GATE will look like:

The startNode and endNode are created from offsets referring the beginning and the end of ‘A piece of text’ in the document’s content.

The documents supported by GATE have to be in one of the encodings accepted by Java. The most popular is the ‘UTF-8’ encoding which is also the most storage efficient one for UNICODE. If, when loading a document in GATE the encoding parameter is set to ‘’(the empty string), then the default encoding of the platform will be used.

5.5.1 Detecting the Right Reader [#]

In order to successfully apply the document creation algorithm described above, GATE needs to detect the proper reader to use for each document format. If the user knows in advance what kind of document they are loading then they can specify the MIME type (e.g. text/html) using the init parameter mimeType, and GATE will respect this. If an explicit type is not given, GATE attempts to determine the type by other means, taking into consideration (where possible) the information provided by three sources:

• Document’s extension
• The web server’s content type
• Magic numbers detection

The first represents the extension of a file like (xml,htm,html,txt,sgm,rtf, etc), the second represents the HTTP information sent by a web server regarding the content type of the document being send by it (text/html; text/xml, etc), and the third one represents certain sequences of chars which are ultimately number sequences. GATE is capable of supporting multimedia documents, if the right reader is added to the framework. Sometimes, multimedia documents are identified by a signature consisting in a sequence of numbers. Inside GATE they are called magic numbers. For textual documents, certain char sequences form such magic numbers. Examples of magic numbers sequences will be provided in the Input section of each format supported by GATE.

All those tests are applied to each document read, and after that, a voting mechanism decides what is the best reader to associate with the document. There is a degree of priority for all those tests. The document’s extension test has the highest priority. If the system is in doubt which reader to choose, then the one associated with document’s extension will be selected. The next higher priority is given to the web server’s content type and the third one is given to the magic numbers detection. However, any two tests that identify the same mime type, will have the highest priority in deciding the reader that will be used. The web server test is not always successful as there might be documents that are loaded from a local file system, and the magic number detection test is not always applicable. In the next paragraphs we will se how those tests are performed and what is the general mechanism behind reader detection.

The method that detects the proper reader is a static one, and it belongs to the gate.DocumentFormat class. It uses the information stored in the maps filled by the init() method of each reader. This method comes with three signatures:

The first two methods try to detect the right MimeType for the GATE document, and after that, they call the third one to return the reader associate with a MimeType. Of course, if an explicit mimeType parameter was specified, GATE calls the third form of the method directly, passing the specified type. GATE uses the implementation from ‘http://jigsaw.w3.org’ for mime types.

The magic numbers test is performed using the information form
magic2mimeTypeMap map. Each key from this map, is searched in the first bufferSize (the default value is 2048) chars of text. The method that does this is called
runMagicNumbers(InputStreamReader aReader) and it belongs to DocumentFormat class. More details about it can be found in the GATE API documentation.

In order to activate a reader to perform the unpacking, the creole definition of a GATE document defines a parameter called ‘markupAware’ initialized with a default value of true. This parameter, forces GATE to detect a proper reader for the document being read. If no reader is found, the document’s content is load and presented to the user, just like any other text editor (this for textual documents).

The next subsections investigates particularities for each format and will describe the file extensions registered with each document format.

5.5.2 XML [#]

Input [#]

GATE permits the processing of any XML document and offers support for XML namespaces. It benefits the power of Apache’s Xerces parser and also makes use of Sun’s JAXP layer. Changing the XML parser in GATE can be achieved by simply replacing the value of a Java system property (‘javax.xml.parsers.SAXParserFactory’).

GATE will accept any well formed XML document as input. Although it has the possibility to validate XML documents against DTDs it does not do so because the validating procedure is time consuming and in many cases it issues messages that are annoying for the user.

There is an open problem with the general approach of reading XML, HTML and SGML documents in GATE. As we previously said, the text covered by tags/elements is appended to the GATE document content and a GATE annotation refers to this particular span of text. When appending, in cases such as ‘end.</P><P>Start’ it might happen that the ending word of the previous annotation is concatenated with the beginning phrase of the annotation currently being created, resulting in a garbage input for GATE processing resources that operate at the text surface.

Let’s take another example in order to better understand the problem:

When the markup is transformed to annotations, it is likely that the text from the document’s content will be as follows:

This is a titleThis is a paragraphHere is an useful link

The annotations created will refer the right parts of the texts but for the GATE’s processing resources like (tokenizer, gazetter, etc) which work on this text, this will be a major disaster. Therefore, in order to prevent this problem from happening, GATE checks if it’s likely to join words and if this happens then it inserts a space between those words. So, the text will look like this after loaded in GATE Developer:

This is a title This is a paragraph Here is an useful link

There are cases when these words are meant to be joined, but they are rare. This is why it’s an open problem.

The extensions associate with the XML reader are:

• xml
• xhtm
• xhtml

The web server content type associate with xml documents is: text/xml.

The magic numbers test searches inside the document for the XML(<?xml version="1.0") signature. It is also able to detect if the XML document uses the semantics described in the GATE document format DTD (see 5.5.2 below) or uses other semantics.

Output [#]

GATE is capable of ensuring persistence for its resources. The types of persistent storage used for Language Resources are:

• Java serialization;
• XML serialization.

We describe the latter case here.

XML persistence doesn’t necessarily preserve all the objects belonging to the annotations, documents or corpora. Their features can be of all kinds of objects, with various layers of nesting. For example, lists containing lists containing maps, etc. Serializing these arbitrary data types in XML is not a simple task; GATE does the best it can, and supports native Java types such as Integers and Booleans, but where complex data types are used, information may be lost(the types will be converted into Strings). GATE provides a full serialization of certain types of features such as collections, strings and numbers. It is possible to serialize only those collections containing strings or numbers. The rest of other features are serialized using their string representation and when read back, they will be all strings instead of being the original objects. Consequences of this might be observed when performing evaluations (see Chapter 10).

When GATE outputs an XML document it may do so in one of two ways:

• When the original document that was imported into GATE was an XML document, GATE can dump that document back into XML (possibly with additional markup added);
• For all document formats, GATE can dump its internal representation of the document into XML.

In the former case, the XML output will be close to the original document. In the latter case, the format is a GATE-specific one which can be read back by the system to recreate all the information that GATE held internally for the document.

In order to understand why there are two types of XML serialization, one needs to understand the structure of a GATE document. GATE allows a graph of annotations that refer to parts of the text. Those annotations are grouped under annotation sets. Because of this structure, sometimes it is impossible to save a document as XML using tags that surround the text referred to by the annotation, because tags crossover situations could appear (XML is essentially a tree-based model of information, whereas GATE uses graphs). Therefore, in order to preserve all annotations in a GATE document, a custom type of XML document was developed.

The problem of crossover tags appears with GATE’s second option (the preserve format one), which is implemented at the cost of losing certain annotations. The way it is applied in GATE is that it tries to restore the original markup and where it is possible, to add in the same manner annotations produced by GATE.

How to Access and Use the Two Forms of XML Serialization

Save as XML Option

This option is available in GATE Developer in the pop-up menu associated with each language resource (document or corpus). Saving a corpus as XML is done by calling ‘Save as XML’ on each document of the corpus. This option saves all the annotations of a document together their features(applying the restrictions previously discussed), using the GateDocument.dtd :

The document is saved under a name chosen by the user and it may have any extension. However, the recommended extension would be ‘xml’.

Using GATE Embedded, this option is available by calling gate.Document’s toXml() method. This method returns a string which is the XML representation of the document on which the method was called.

Note: It is recommended that the string representation to be saved on the file system using the UTF-8 encoding, as the first line of the string is : <?xml version="1.0" encoding="UTF-8"?>

Example of such a GATE format document:

Note: One must know that all features that are not collections containing numbers or strings or that are not numbers or strings are discarded. With this option, GATE does not preserve those features it cannot restore back.

The Preserve Format Option This option is available in GATE Developer from the popup menu of the annotations table. If no annotation in this table is selected, then the option will restore the document’s original markup. If certain annotations are selected, then the option will attempt to restore the original markup and insert all the selected ones. When an annotation violates the crossed over condition, that annotation is discarded and a message is issued.

This option makes it possible to generate an XML document with tags surrounding the annotation’s referenced text and features saved as attributes. All features which are collections, strings or numbers are saved, and the others are discarded. However, when read back, only the attributes under the GATE namespace (see below) are reconstructed back differently to the others. That is because GATE does not store in the XML document the information about the features class and for collections the class of the items. So, when read back, all features will become strings, except those under the GATE namespace.

One will notice that all generated tags have an attribute called ‘gateId’ under the namespace ‘http://www.gate.ac.uk’. The attribute is used when the document is read back in GATE, in order to restore the annotation’s old ID. This feature is needed because it works in close cooperation with another attribute under the same namespace, called ‘matches’. This attribute indicates annotations/tags that refer the same entity¹. They are under this namespace because GATE is sensitive to them and treats them differently to all other elements with their attributes which fall under the general reading algorithm described at the beginning of this section.

The ‘gateId’ under GATE namespace is used to create an annotation which has as ID the value indicated by this attribute. The ‘matches’ attribute is used to create an ArrayList in which the items will be Integers, representing the ID of annotations that the current one matches.

Example:

If the text being processed is as follows:

What GATE does when it parses this text is it creates two annotations:

Under GATE Embedded, this option is available by calling gate.Document’s toXml(Set aSetContainingAnnotations) method. This method returns a string which is the XML representation of the document on which the method was called. If called with null as a parameter, then the method will attempt to restore only the original markup. If the parameter is a set that contains annotations, then each annotation is tested against the crossover restriction, and for those found to violate it, a warning will be issued and they will be discarded.

In the next subsections we will show how this option applies to the other formats supported by GATE.

5.5.3 HTML [#]

Input

HTML documents are parsed by GATE using the NekoHTML parser. The documents are read and created in GATE the same way as the XML documents.

The extensions associate with the HTML reader are:

• htm
• html

The web server content type associate with html documents is: text/html.

The magic numbers test searches inside the document for the HTML(<html) signature.There are certain HTML documents that do not contain the HTML tag, so the magical numbers test might not hold.

There is a certain degree of customization for HTML documents in that GATE introduces new lines into the document’s text content in order to obtain a readable form. The annotations will refer the pieces of text as described in the original document but there will be a few extra new line characters inserted.

After reading H1, H2, H3, H4, H5, H6, TR, CENTER, LI, BR and DIV tags, GATE will introduce a new line (NL) char into the text. After a TITLE tag it will introduce two NLs. With P tags, GATE will introduce one NL at the beginning of the paragraph and one at the end of the paragraph. All newly added NLs are not considered to be part of the text contained by the tag.

Output

The ‘Save as XML’ option works exactly the same for all GATE’s documents so there is no particular observation to be made for the HTML formats.

When attempting to preserve the original markup formatting, GATE will generate the document in xhtml. The html document will look the same with any browser after processed by GATE but it will be in another syntax.

5.5.4 SGML [#]

Input

The SGML support in GATE is fairly light as there is no freely available Java SGML parser. GATE uses a light converter attempting to transform the input SGML file into a well formed XML. Because it does not make use of a DTD, the conversion might not be always good. It is advisable to perform a SGML2XML conversion outside the system(using some other specialized tools) before using the SGML document inside GATE.

The extensions associate with the SGML reader are:

• sgm
• sgml

The web server content type associate with xml documents is : text/sgml.

There is no magic numbers test for SGML.

Output

When attempting to preserve the original markup formatting, GATE will generate the document as XML because the real input of a SGML document inside GATE is an XML one.

5.5.5 Plain text [#]

Input

When reading a plain text document, GATE attempts to detect its paragraphs and add ‘paragraph’ annotations to the document’s ‘Original markups’ annotation set. It does that by detecting two consecutive NLs. The procedure works for both UNIX like or DOS like text files.

Example:

If the plain text read is as follows:

then two ‘paragraph’ type annotation will be created in the ‘Original markups’ annotation set (referring the first and second paragraphs ) with an empty feature map.

The extensions associate with the plain text reader are:

• txt
• text

The web server content type associate with plain text documents is: text/plain.

There is no magic numbers test for plain text.

Output

When attempting to preserve the original markup formatting, GATE will dump XML markup that surrounds the text refereed.

The procedure described above applies both for plain text and RTF documents.

5.5.6 RTF [#]

Input

Accessing RTF documents is performed by using the Java’s RTF editor kit. It only extracts the document’s text content from the RTF document.

The extension associate with the RTF reader is ‘rtf’.

The web server content type associate with xml documents is : text/rtf.

The magic numbers test searches for {\\rtf1.

Output

Same as the plain tex output.

5.5.7 Email [#]

Input

GATE is able to read email messages packed in one document (UNIX mailbox format). It detects multiple messages inside such documents and for each message it creates annotations for all the fields composing an e-mail, like date, from, to, subject, etc. The message’s body is analyzed and a paragraph detection is performed (just like in the plain text case) . All annotation created have as type the name of the e-mail’s fields and they are placed in the Original markup annotation set.

Example:

GATE attempts to detect lines such as ‘From someone@zzz.zzz.zzz Wed Sep 6 10:35:50 2000’ in the e-mail text. Those lines separate e-mail messages contained in one file. After that, for each field in the e-mail message annotations are created as follows:

The annotation type will be the name of the field, the feature map will be empty and the annotation will span from the end of the field until the end of the line containing the e-mail field.

Example:

The extensions associated with the email reader are:

• eml
• email
• mail

The web server content type associate with plain text documents is: text/email.

The magic numbers test searches for keywords like Subject:,etc.

Output

Same as plain text output.

5.6 XML Input/Output [#]

Support for input from and output to XML is described in Section 5.5.2. In short:

• GATE will read any well-formed XML document (it does not attempt to validate XML documents). Markup will by default be converted into native GATE format.
• GATE will write back into XML in one of two ways:
  1. Preserving the original format and adding selected markup (for example to add the results of some language analysis process to the document).
  2. In GATE’s own XML serialisation format, which encodes all the data in a GATE Document (as far as this is possible within a tree-structured paradigm – for 100% non-lossy data storage use GATE’s RDBMS or binary serialisation facilities – see Section 4.5).

When using GATE Embedded, object representations of XML documents such as DOM or jDOM, or query and transformation languages such as X-Path or XSLT, may be used in parallel with GATE’s own Document representation (gate.Document) without conflicts.

Chapter 6
ANNIE: a Nearly-New Information Extraction System [#]

GATE was originally developed in the context of Information Extraction (IE) R&D, and IE systems in many languages and shapes and sizes have been created using GATE with the IE components that have been distributed with it (see [Maynard et al. 00] for descriptions of some of these projects).¹

GATE is distributed with an IE system called ANNIE, A Nearly-New IE system (developed by Hamish Cunningham, Valentin Tablan, Diana Maynard, Kalina Bontcheva, Marin Dimitrov and others). ANNIE relies on finite state algorithms and the JAPE language (see Chapter 8).

ANNIE components form a pipeline which appears in figure 6.1.

ANNIE components are included with GATE (though the linguistic resources they rely on are generally more simple than the ones we use in-house). The rest of this chapter describes these components.

6.1 Document Reset [#]

The document reset resource enables the document to be reset to its original state, by removing all the annotation sets and their contents, apart from the one containing the document format analysis (Original Markups). An optional parameter, keepOriginalMarkupsAS, allows users to decide whether to keep the Original Markups AS or not while reseting the document. This resource is normally added to the beginning of an application, so that a document is reset before an application is rerun on that document.