\documentclass[10pt,a4paper]{article}
\usepackage{hhline,url,moreverb}
\usepackage[square,numbers]{natbib}
\usepackage[british]{babel}
\usepackage[T1]{fontenc} % edh in BibTeX
\usepackage{graphicx}
\newcommand{\sluga}{\rule{1.5em}{0em}}
\newcommand{\slugb}{\rule{1.0em}{0em}}
\newcommand{\slugc}{\rule{1.0em}{0em}}
\urldef\mails\url|initial.surname@dcs.shef.ac.uk|
\selectlanguage{british}
%
\title{An Entity-Centric Approach to Storing Community Memories}

\author{Diana Maynard, Wim Peters, Jonathon Hare, Adam Funk\\
  University of Sheffield, Department of Computer Science\\
  Regent Court, 211 Portobello, Sheffield, S1~4DP, UK\\
  \mails}

%
\begin{document}
\maketitle
%
% \begin{abstract}
%   
% \end{abstract}
%
% Your abstract must consist of 1500 to 2000 words (including references)
% and must be formatted in PDF. 
% An abstract of 1500-2000 words is about 3-4 pages and can contain references, tables, figures.
% The submissions are NOT anonymous

\section{Introduction}
\label{sec:intro}

%% this needs to be modified a bit

With the rapidly growing volume of resources on the Web, Web archiving becomes an important challenge. In addition, the notion of community memories extends traditional Web archives with related data from a variety of sources on the Social Web. Community memories take an entity-centric view to organise Web content according to the events, entities and opinions related to them. To this end, the main challenge is to extract, detect and correlate such information from a vast number of heterogeneous Web resources, including multimedia. The ARCOMEM project aims to perform this task based on an iterative cycle consisting of (1) targeted archiving/crawling of Web objects, (2) entity, topic, opinion and event (ETOE) extraction and  and (3) refinement of crawling strategy.  Within this paper, we describe the ETOE extraction component.


\section{System Architecture}

\begin{figure}[tb]
\begin{center}
\includegraphics[width=\textwidth]{architecture-etoes}
\end{center}
\caption{Architecture of the ETOE component}
\label{fig:etoes-architecture}
\end{figure}

The ETOE Detection architecture is shown in Figure \ref{fig:etoes-architecture}. It passes over a single crawled web object in a simplified form (containing structural information, links, information about embedded multimedia objects and the text itself. 
The first step in the ETOE detection process is to determine the language of the text for all subsequent linguistic processing, using either metadata from the text or a language identification component. The Linguistic Preprocessing provides linguistic information that will be used in later processing, such as tokenisation, part of speech tagging, lemmatization and so on. Next, the Entity Detection module tries to detect crawl-related entities that occur in the document, followed by relation detection, which is used together with the entity information for the detection of events.

Beside text documents, the ETOE component also analyses images and videos to predict entities within images, as well as topics and events depicted by the image as a whole. Images can potentially be very useful in aiding the disambiguation of entities extracted from the textual content of the document. The training and data collection for these classifiers is an offline process that is itself guided by the crawl specification. For the processing of videos, images are first extracted from the video and then picture analysis methods are applied.

The extracted meta-information and the linguistically tagged document are also used for detecting the opinion or sentiment of the document. Then, topic detection builds upon the results of the previous processing to identify the topic. Finally, the document will be annotated with all extracted information and used as a starting point for the dynamics analysis, social web analysis and relevance analysis modules. In the following sections, we describe the main individual ETOE components in more detail. Full description of these (and their evaluation) will be included in the final paper.

\section{Entity extraction}
The entity extraction component of the system makes use of the named entity recognition tools in GATE, which have been modified and extended for the purposes of this task. The set of entities has been extended to include:
\begin{itemize}
\item Persons (e.g. artists, politicans, web 2.0 users);
\item Organizations (e.g. companies, music bands, political parties);
\item Locations (e.g. location names, geo codes);
\item Events (e.g. concerts, accidents, crimes);
\item Brands and products (e.g. swr3, Tatort, iPhone);
\item Dates and times (of events and content publication).
\end{itemize}

Entity extraction also includes the recognition of technical terms, for which we have adapted and extended the TermRaider plugin for GATE. Both named entity and term recognition tools have also been adapted to work on German as well as English texts. The application can deal with a multilingual corpus, making use of a language identification plugin to first determine whether a piece of text is in English or German, and then calling the appropriate language processing components (tokeniser, POS tagger, named entity recognition components, etc.) for that language. In this way, the selection of appropriate resources for the language is entirely automatic and requires no human intervention.


\section{Event detection}

In this work, we refer to an event as a situation within the domain (states, actions, processes,
properties) expressed by one or more relations. These may be unique events such as the first landing on the moon or a natural disaster, or regularly occurring events such as elections or TV serials. 

Events can be expressed by text elements such as:
\begin{itemize}
\item verbal predicates and their arguments  (e.g. ``The committee dismissed the proposal'');
\item noun  phrases  headed  by  nominalizations  (e.g. ``economic
  growth'');
\item adjective-noun combinations (e.g. ``governmental measure''; ``public money'');
\item event-referring nouns (e.g. ``crisis'', ``cash injection'').
\end{itemize}

Events can denote  different  levels  of  semantic  granularity, i.e. general  events  can  contain more specific sub-events.  For  instance,  the  performances  of various  bands  form  sub-events of a wider music  event, while a general event like ``Turkey's EU accession'' has sub-events such as the European Parliament approving Turkey's Progress Report.

The event detection component consists of a combination of two approaches. The top-down approach involves a form of template filling, by selecting a number of known events in advance, and then identifying relevant verbs and their subjects and objects to match the slots. For example, a "performance" event might consist of a band name, a verb denoting some kind of "performing" verb, and optionally a date and location. This kind of approach tends to produce high precision but relatively low recall. We therefore supplement this with a bottom-up approach which consists of identifying verbal relations in the text, and classifying them into semantic categories, from which new events can be suggested. This kind of approach produces higher recall, but precision can be low due to attachment ambiguities if full parsing is not used. We tend to avoid full parsing because, in addition to being slow, it is often highly inaccurate on social media where sentences are not grammatical.

\section{Opinion Mining}

The opinion mining component finds opinions and sentiments relating to the previously identified entities and events. The first stage is to perform a basic sentiment analysis, i.e., to associate a positive, negative or neutral sentiment with each relevant entity or event. We extend the work of \cite{Maynard11a} which looked at identifying political opinions in tweets, and associating with each relevant tweet a triple denoting three kinds of entity: Person, Opinion and Political Party. Here, the opinion is extended to all kinds of entity and event, not just to political parties.

The detection of the actual opinion (sentiment) is performed via a number of different phases: detecting positive, negative and neutral words, identifying factual or opinionated versus questions or doubtful statements, identifying negatives, sarcasm and irony, and detecting extra-linguistic clues such as smileys. These processing resources are developed for both German and English, and make use of SentiWS \cite{Remus2010} for German and SentiWordNet \cite{Esuli2006} for English as seed lexicons for opinionated words. The initial system uses a rule-based approach, but integration of additional machine learning components is planned.

\section{Multimedia Processing}
Many of the processes that can be applied to textual information have analogues in multimedia data. For example, entity extraction is analogous to object recognition (or face recognition for \textit{person} entities) and topic detection is analogous to multimedia summarisation. Recent work has even investigated how sentiment analysis can be applied to mine coarse notions of opinion from images \cite{Siersdorfer2010}.

Unconstrained object and face recognition in images and video is probably impossible. However, by adding constraints based on the entities in the text surrounding the multimedia object, the problem is much more tractable. An interesting side effect of combining the textual and multimedia information  is that the multimedia data can be used to enrich and disambiguate the textual information and vice versa. As an example, consider a news article about ``George Bush'' that includes a picture. The text of the article may not explicitly make it clear which George Bush (junior or senior) is being discussed, however, by applying face recognition/verification the it becomes possible to disambiguate the entity extracted from the text. We are actively extending OpenIMAJ to classify more complex objects and scene types using combined visual and textual features, in particular we are investigating new techniques for dynamically building object classifiers for previously unseen object types.

Another potential use for the multimedia data is to create links between documents that reuse the same, or a similar, multimedia object. Near-duplicate detection and matching of multimedia objects is a relatively advanced area of research \cite{Lowe2004,Zhou2010}. In terms of archiving, this is useful where articles occur in languages that are not supported by the NLP tools; if an image that occurs in an English article that has been deemed highly relevant based on features extracted through NLP, and the same image occurs in a different article, written in an unsupported language, then it is highly likely that the second article is also relevant to the archive. 

%%%%%
\section{Conclusions}

This abstract has sketched the main extraction components for an entity-centric approach to community archiving. The system extends a core IE system to handle complex events, opinion mining and multimedia components, all of which provide further information which is relevant for better understanding of the text, and which feeds back into the crawler and storage mechanisms for the archiving tool. Preliminary evaluations of the individual components are promising; further details will be provided in the full paper. 


%%%%%

%%%%%

%%%%%
\bibliographystyle{plainnat}
\bibliography{../../big}
\end{document}
