Saliha Azzam

Event coreference for information extraction

We propose a general approach for performing event coreference and for constructing complex event representations, such as those required for information extraction tasks. Our approach is based on a representation which allows a tight coupling between world or conceptual modelling and discourse modelling. The representation and the coreference mechanism are fully implemented within the LaSIE information extraction system where the mechanism is used for both object (noun phrase) and event coreference resolution. Indirect evaluation of the approach shows small, but significant benefit, for information extraction tasks.

Using coreference chains for text summarization

We describe the use of coreference chains for the production of text summaries, using a variety of criteria to select a best chain to represent the main topic of a text. The approach has been implemented within an existing MUC coreference system, which constructs a full discourse model of texts, including information about changes of focus, which can be used in the selection of chains. Some preliminary experiments on the automatic evaluation of summaries are also described, using existing tools to attempt to replicate some of the recent SUMMAC manual evaluations.

Evaluating a Focus-Based Approach to Anaphora Resolution

We present an approach to anaphora resolution based on a focusing algorithm, and implemented within an existing MUC (Message Understanding Conference) Information Extraction system, allowing quantitative evaluation against a substantial corpus of annotated real-world texts. Extensions to the basic focusing mechanism can be easily tested, resulting in refinements to the mechanism and resolution rules. Results show that the focusing algorithm is highly sensitive to the quality of syntactic-semantic analyses, when compared to a simpler heuristic-based approach.

Building Up and Making Use of Corporate Knowledge Repositories

In this paper, we present a methodology (and some concrete experiments) for the construction and use of corporate knowledge repositories. They can be defined as on-line, computer-based storehouses of expertise, knowledge, experience and documentation about particular aspects of a corporation. We consider here only the ‘textual component’ of corporate knowledge — i.e., all sorts of economically valuable, natural language documents like news stories, telex reports, internal documentation (memos, policy statements, reports and minutes), normative texts, intelligence messages, etc. In this case, the construction of effectively usable corporate knowledge repositories can be achieved with the translation of the original documents into some type of conceptual format. The ‘metadocuments’ obtained in this way can then be stored into a knowledge repository and, given their role of advanced document models, all the traditional functions of information retrieval, e.g., searching, retrieving and producing an answer (and other functions like intelligent navigation inside the repository) can be performed directly on them.

Using a language independent domain model for multilingual information extraction

The volume of electronic text in different languages, particularly on the World Wide Web, is growing significantly, and the problem of users who are restricted in the number of languages they read obtaining information from this text is becoming more widespread. This article investigates some of the issues involved in achieving multilingual information extraction (IE), describes the approach adopted in the M-LaSIE-II IE system, which addresses these problems, and presents the results of evaluating the approach against a small parallel corpus of English/French newswire texts. The approach is based on the assumption that it is possible to construct a language independent representation of concepts relevant to the domain, at least for the small well-defined domains typical of IE tasks, allowing multilingual IE to be successfully carried out without requiring full machine translation.

Resolving Anaphors in Embedded Sentences

We propose an algorithm to resolve anaphors, tackling mainly the problem of intrasentential antecedents. We base our methodology on the fact that such antecedents are likely to occur in embedded sentences. Sidners focusing mechanism is used as the basic algorithm in a more complete approach. The proposed algorithm has been tested and implemented as a part of a conceptual analyser, mainly to process pronouns. Details of an evaluation are given.