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We describe GATE, the General Architecture for Text Engineering, an integrated visual development environment to support the visual assembly, execution and analysis of modular natural language processing systems. The visual model is an executable data flow program graph, automatically synthesised from data dependency declarations of language processing modules. The graph is then directly executable: modules are run interactively in the graph, and results are accessible via generic text visualisation tools linked to the modules. These tools lighten the `cognitive load? of viewing and comparing module results by relating data produced by modules back to the underlying text, by reducing the amount of search in examining results, and by displaying results in context. Overall, the GATE integrated visual development environment leads to rapid understanding of system behaviour and hence to rapid system refinement, therefore demonstrating the utility of visual programming and visualisation techniques for the development of natural language processing systems.

Information extraction technology, as defined and developed through the US Defense Advanced Research Projects Agency (DARPA) Message Understanding Conferences (MUCs), has proved successful at extracting information primarily from newswire texts and primarily in domains concerned with human activity. In this paper, the application of this technology to the extraction of information from scientific journal papers in the area of molecular biology is considered. In particular, it is described how an information extraction designed to participate in the MUC exercises has been modified for two bioinformatics applications: one concerned with enzyme and metabolic pathways; the other with protein structure. Progress to date provides convincing grounds for believing that information extraction techniques will deliver novel and effective ways for scientists to make use of the core literature which defines their disciplines.

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.

We describe SUPPLE, a freely-available, open source natural language parsing system, implemented in Prolog, and designed for practical use in language engineering (LE) applications. SUPPLE can be run as a stand-alone application, or as a component within the GATE General Architecture for Text Engineering. SUPPLE is distributed with an example grammar that has been developed over a number of years across several LE projects. This paper describes the key characteristics of the parser and the distributed grammar.

The recent cloning of two GABA(B) receptor subunits, GABA(B1) and GABA(B2), has raised the possibility that differences in GABA(B) receptor subunit composition may give rise to pharmacologically or functionally distinct receptors. If present, such molecular diversity could permit the selective targeting of GABA(B) receptor subtypes specifically involved in pathologies such as drug addiction, spasticity, pain, and epilepsy. To address these issues we have developed a GABA(B1) subunit knockout mouse using gene targeting techniques. In the brains of GABA(B1) null mice, all pre- and postsynaptic GABA(B) receptor function was absent demonstrating that the GABA(B1) subunit is essential for all GABA(B) receptor-mediated mechanisms. Despite this, GABA(B1) null mice appeared normal at birth, although by postnatal week four their growth was retarded and they developed a generalized epilepsy that resulted in premature death. In addition, GABA(B1) heterozygote animals showed enhanced prepulse inhibition responses compared to littermate controls, suggesting that GABA(B1) deficient mice exhibit increased sensorimotor gating mechanisms. These data suggest that GABA(B) receptor antagonists may be of benefit in the treatment of psychiatric and neurological disorders in which attentional processing is impaired.

A relatively self-contained subtask of natural language generation is sentence realization: the process of generating a grammatically correct sentence from an abstract semantic / logical representation. We propose a method where sentence realization is carried out using a simplified (context free) version of a large analysis grammar, combined with a statistical language model from the full (context sensitive) version of the same grammar. The statistical model provides a measure of the probability of syntactic substructures, derived from the analysis of a corpus with the full grammar, and is used to guide both subsequent analysis and generation.

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.

The authors describe GGI, a visual system that allows the user to execute an automatically generated data flow graph containing code modules that perform natural language processing tasks. These code modules operate on text documents. GGI has a suite of text visualisation tools that allows the user useful views of the annotation data that is produced by the modules in the executable graph. GGI forms part of the GATE natural language engineering system.

We classify and review current approaches to software infrastructure for research, development and delivery of NLP systems. The task is motivated by a discussion of current trends in the field of NLP and Language Engineering. We describe a system called GATE (a General Architecture for Text Engineering) that provides a software infrastructure on top of which heterogeneous NLP processing modules may be evaluated and refined individually, or may be combined into larger application systems. GATE aims to support both researchers and developers working on component technologies (e.g. parsing, tagging, morphological analysis) and those working on developing end-user applications (e.g. information extraction, text summarisation, document generation, machine translation, and second language learning). GATE promotes reuse of component technology, permits specialisation and collaboration in large-scale projects, and allows for the comparison and evaluation of alternative technologies. The first release of GATE is now available - see http://www.dcs.shef.ac.uk/research/groups/nlp/gate/, LaTeX, uses aclap.sty, 8 pages

Given an information extraction (IE) system that performs an extraction task against texts in one language, it is natural to consider how to modify the system to perform the same task against texts in a different language. More generally, there may be a requirement to do the extraction task against texts in an arbitrary number of different languages and to present results to a user who has no knowledge of the source language from which the information has been extracted. To minimise the language-specific alterations that need to be made in extending the system to a new language, it is important to separate the task-specific conceptual knowledge the system uses, which may be assumed to be language independent, from the language-dependent lexical knowledge the system requires, which unavoidably must be extended for each new language. In this paper we describe how the architecture of the LaSIE system, an IE system designed to do monolingual extraction from English texts, has been modified to support a clean separation between conceptual and lexical information. This separation allows hard-to-acquire, domain-specific conceptual knowledge to be represented only once, and hence to be reused in extracting information from texts in multiple languages, while standard lexical resources can be used to extend language coverage. Preliminary experiments with extending the system to French are described.

Much progress has been made in the provision of reusable data resources for Natural Language Engineering, such as grammars, lexicons, thesauruscs. Although a number of projects have addressed the provision of reusable algorithmic resources (or 'tools'), takeup of these resources has been relatively slow. This paper describes GATE, a General Architecture for Text Engineering, which is a freely-available system designed to help alleviate the problem.

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.

The LaSIE (Large Scale Information Extraction) system has been developed at the University of Sheffield as part of an ongoing research effort into information extraction and, more generally, natural language engineering.

We describe a software environment to support research and development in natural language (NL) engineering. This environment -- GATE (General Architecture for Text Engineering) -- aims to advance research in the area of machine processing of natural languages by providing a software infrastructure on top of which heterogeneous NL component modules may be evaluated and refined individually or may be combined into larger application systems. Thus, GATE aims to support both researchers and developers working on component technologies (e.g. parsing, tagging, morphological analysis) and those working on developing end-user applications (e.g. information extraction, text summarisation, document generation, machine translation, and second language learning). GATE will promote reuse of component technology, permit specialisation and collaboration in large-scale projects, and allow for the comparison and evaluation of alternative technologies. The first release of GATE is now available.

Information extraction technology, as defined and developed through the U.S. DARPA Message Understanding Conferences (MUCs), has proved successful at extracting information primarily from newswire texts and primarily in domains concerned with human activity. In this paper we consider the application of this technology to the extraction of information from scientific journal papers in the area of molecular biology. In particular, we describe how an information extraction system designed to participate in the MUC exercises has been modified for two bioinformatics applications: EMPathIE, concerned with enzyme and metabolic pathways; and PASTA, concerned with protein structure. Progress to date provides convincing grounds for believing that IE techniques will deliver novel and effective ways for scientists to make use of the core literature which defines their disciplines.