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GATE, a General Architecture for Text Engineering

GATE, a General Architecture for Text Engineering Hamish Cunningham, Kalina Bontcheva Department of Computer Science, University of Sheffield Wednesday October 30 th 2002 Next generation web GATE, language technology infrastructure. 1(20). A Ubiquitous Permeable Web

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GATE, a General Architecture for Text Engineering

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  1. GATE, a General Architecture for Text Engineering • Hamish Cunningham, Kalina BontchevaDepartment of Computer Science, • University of Sheffield • Wednesday October 30th 2002 • Next generation web • GATE, language technology infrastructure 1(20)

  2. A Ubiquitous Permeable Web • The next generation of the web must be: • ubiquitous: semantics for every device, every organisation, every individual; • permeable: allow contextual data to penetrate and persist; • companionable: able to engage with us via multiple natural modalities. • Roles for Language Technology: • discovery of semantics (ubiquity); • mediating between context and personal semantic memories (permeability); • conversing with people and the semantic web (companionableness). 2(20)

  3. Critical Mass for the Semantic Web • The SW: machine processable, repurposable data to compliment hypertext • But: semantics = 0.0000000...% of the Web • How to achieve critical mass? Huge scale automatic annotation. Requirements: • Huge scale:– freely available to all EU citizens– distributed (over a Grid)– re-purposeable (delivered as Web Services) • Portability and robustness via:– simple and therefore shallow HLT methods– +ve and –ve learning– analogs of IPSEs for computer-literate users 3 (20)

  4. Motivation for Software Infrastructure for Language Engineering • Need for scalable, reusable, and portable HLT solutions • Support for large data, in multiple media, languages, formats, and locations • Lowering the cost of creation of new language processing components • Promoting quantitative evaluation metrics via tools and a level playing field 4 (20)

  5. Motivation (II): software lifecycle in collaborative research • Project Proposal: We love each other. We can work so well together. We can hold workshops on Santorini together. We will solve all the problems of AI that our predecessors were too stupid to. • Analysis and Design: Stop work entirely, for a period of reflection and recuperation following the stress of attending the kick-off meeting in Luxembourg. • Implementation: Each developer partner tries to convince the others that program X that they just happen to have lying around on a dusty disk-drive meets the project objectives exactly and should form the centrepiece of the demonstrator. • Integration and Testing: The lead partner gets desperate and decides to hard-code the results for a small set of examples into the demonstrator, and have a fail-safe crash facility for unknown input ("well, you know, it's still a prototype..."). • Evaluation: Everyone says how nice it is, how it solves all sorts of terribly hard problems, and how if we had another grant we could go on to transform information processing the World over (or at least the European business travel industry). 2(20)

  6. GATE, a General Architecture for Text Engineering • An architectureA macro-level organisational picture for LE software systems. • A frameworkFor programmers, GATE is an object-oriented class library that implements the architecture. • A development environmentFor language engineers, computational linguists et al, GATE is a graphical development environment bundled with a set of tools for doing e.g. Information Extraction. • Some free components... ...and wrappers for other people's components • Tools for: evaluation; visualise/edit; persistence; IR; IE; dialogue; ontologies; etc. • Free software (LGPL). Download at http://gate.ac.uk/download/ 6 (20)

  7. Architectural principles • Non-prescriptive, theory neutral (strength and weakness) • Re-use, interoperation, not reimplementation (e.g. diverse XML support, integration of tools like Protégé, Jena and Weka) • (Almost) everything is a component, and component sets are user-extendable • Component-based development • An OO way of chunking software: Java Beans • GATE components: CREOLE = modified Java Beans (Collection of REusable Objects for Language Engineering) • The minimal component = 10 lines of Java, 10 lines of XML, 1 URL. 7 (20)

  8. GATE Language Resources • GATE LRs are documents, ontologies, corpora, lexicons, …… • Documents / corpora: • GATE documents loaded from local files or the web... • Diverse document formats: text, html, XML, email, RTF, SGML. • Processing Resourcres • Algorithmic components knows as PRs – beans with execute methods. • All PRs can handle Unicode data by default. • Clear distinction between code and data (simple repurposing). • 20-30 freebies with GATE • e.g. Named entity recognition; WordNet; Protégé; Ontology; OntoGazetteer; DAML+OIL export; Information Retrieval based on Lucene 8 (20)

  9. GATE Format Handlers HTML docs RTF docs XML docs … ANNIE … Named entity Core- ference Document content Document metadata Document format data Linguistic data POS tagger … Named entity … Event extraction … A Language AnalysisExample Custom application 1 Relational Database File storage Oracle/ PostgresQL

  10. 10(11)

  11. Building IE Components in GATE (1) • The ANNIE system – a reusable and easily extendable set of components 11 (20)

  12. Building IE Components in GATE (2) • JAPE: a Java Annotation Patterns Engine • Light, robust regular-expression-based processing • Cascaded finite state transduction • Low-overhead development of new components • Rule: Company1 • Priority: 25 • ( • ( {Token.orthography == upperInitial} )+ • {Lookup.kind == companyDesignator} • ):companyMatch • --> • :companyMatch.NamedEntity = { kind = company, rule = “Company1” } 12 (20)

  13. Performance Evaluation • At document level – annotation diff • At corpus level – corpus benchmark tool – tracking system’s performance over time 13 (20)

  14. The Semantic Web and GATE • GATE is being used for development of (semi-)automatic methods for: • linking web pages to Ontologies using Information Extraction; • learning and evolving Ontologies via IE and lexical semantic network traversal. 14 (20)

  15. Populating Ontologies with IE

  16. Protégé and Ontology Management

  17. Information Retrieval Support • Based on the Lucene IR engine 17 (20)

  18. Processing Multilingual Data • All the visualisation and editing tools for ML LRs use enhanced Java facilities: 18 (20)

  19. Applications • GATE has been used for a variety of applications, including: • MUMIS: automatic creation of semantic indexes for multimedia programme material • MUSE: a multi-genre IE system • Metadata for Medline (at Merck) • ACE: participation in the Automatic Content Extraction programme • HSE: summarisation of health and safety information from company reports • OldBaileyIE: NE recognition on 17th century Old Bailey Court reports. • AKT: language technology in knowledge management • AMITIES: call centre automation • Various Medical Informatics and database technology projects • IE in Romanian, Bulgarian, Greek, Bengali, Spanish, Swedish, German, Italian, and French (Arabic, Chinese and Russian this autumn) 19 (20)

  20. Conclusion • GATE: an infrastructure that lowers the overhead of creating & embedding robust NLP components • Further information: http://gate.ac.uk/ • Online demos, tutorials and documentation • Software downloads • Talks and papers 20 (20)

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