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 millenium 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 Embeddded. 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 Encitement 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 Version 5.1 beta 1 (Autumn 2009) [#]

To get HTML reports from profiled processing resources, there is a new menu item in the ‘Tools’ menu called ‘Profiling reports’, see chapter 11.

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.

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

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.

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 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.

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.

The Montreal Transducer has been made obsolete.

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

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.

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 13.9 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.

1.5.2 July 2009 (FIG’09 Summer School) [#]

A number of projects took place as part of the FIG’09 summer school:

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.

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 Compositing

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.

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.

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.

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.

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.

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.

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 mechan- ics 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 edit- ing 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 averated 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 (superceded 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) desribes 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.