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Natural language processing in CLIME, a multilingual legal advisory system

Published online by Cambridge University Press:  01 January 2008

ROGER EVANS
Affiliation:
University of Brighton, Brighton BN2 4GJ, UK email: R.P.Evans@brighton.ac.uk
PAUL PIWEK
Affiliation:
Centre for Research in Computing, The Open University, Milton Keynes MK7 6AA, UK email: P.Piwek@open.ac.uk
LYNNE CAHILL
Affiliation:
Department of Linguistics and English Language, University of Sussex, Falmer, Brighton BN1 9QN, UK email: lynneca@sussex.ac.uk
NEIL TIPPER
Affiliation:
Alcatel Austria Aktiengesellschaft, Scheydgasse 41, 1210 Wien, Austria email: Neil.Tipper@alcatel.at

Abstract

This paper describes CLIME, a web-based legal advisory system with a multilingual natural language interface. CLIME is a ‘proof-of-concept’ system which answers queries relating to ship-building and ship-operating regulations. Its core knowledge source is a set of such regulations encoded as a conceptual domain model and a set of formalised legal inference rules. The system supports retrieval of regulations via the conceptual model, and assessment of the legality of a situation or activity on a ship according to the legal inference rules. The focus of this paper is on the natural language aspects of the system, which help the user to construct semantically complex queries using WYSIWYM technology, allow the system to produce extended and cohesive responses and explanations, and support the whole interaction through a hybrid synchronous/asynchronous dialogue structure. Multilinguality (English and French) is viewed simply as interface localisation: the core representations are language-neutral, and the system can present extended or local interactions in either language at any time. The development of CLIME featured a high degree of client involvement, and the specification, implementation and evaluation of natural language components in this context are also discussed.

Type
Papers
Copyright
Copyright © Cambridge University Press 2006

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References

Bertin, A., Bagnato, A. & Lorenzon, D. (2001) Final Evaluation. CLIME (EP25414) Document. WP 5.6 (Circulation restricted to CLIME consortium).Google Scholar
Bertin, A. & Bagnato, A. (1999) Evaluation of the Natural Language Interface. CLIME (EP25414) Document. Task 3.3. (Circulation restricted to CLIME consortium).Google Scholar
Boer, A., Hoekstra, R. & Winkels, R. (2001) The CLIME Ontology. Proceedings of the Second International Workshop on Legal Ontologies, University of Amsterdam, pp. 3747.Google Scholar
Bureau Veritas (1997) Rules and Regulations for the Classification of Ships, Bureau Veritas, Paris, 1997.Google Scholar
Burger, J., Cardie, C., Chaudri, V., Gaizauskas, R., Harabagiu, S., Israel, D., Jacquemin, C., Lin, C.-Y., Maiorano, S., Miller, G., Moldovan, D., Ogden, B., Prager, J., Riloff, E., Singhal, A., Shrihari, R., Strzalkowski, T., Voorhees, E. & Weishedel, R. (2002) Issues, tasks and program structures to roadmap research in question & answering (Q&A), www-nlpir.nist.gov/projects/duc/papers/qa.Roadmap-paper_v2.doc.Google Scholar
Cahill, L. (2000) Semi-automatic construction of multilingual lexicons. Machine Translation and Multilingual Applications in the New Millennium (MT2000), 15: 1–15:10.Google Scholar
Chaudri, V. & Fikes, R. (eds.) (1999) Question Answering Systems. Papers from the 1999 AAAI Fall Symposium, Menlo Park, California, AAAI Press.Google Scholar
Evans, R., Piwek, P. & Cahill, L. (2002) What is NLG? Proceedings of the Second International Conference on Natural Language Generation (INLG-02), New York, ACL, pp. 144154.Google Scholar
Hirschman, L. & Gaizauskas, R. (2001) Natural language question answering: the view from here. Natural Language Engineering 7 (4): 275300.Google Scholar
Krahmer, E. & Theune, M. (2001) Efficient Context sensitive generation of referring expressions. In: van Deemter, K. and Kibble, R. (eds.), Information Sharing: Reference and Presupposition in Language Generation and Interpretation, CSLI Publications, Stanford, pp. 223264.Google Scholar
MARPOL (2002) IMO: MARPOL 73/78, IMO, London.Google Scholar
Piwek, P. (2000) A Formal Semantics for Generating and Editing Plurals. Proceedings of COLING 2000, Saarbruecken, Germany, pp. 607613.Google Scholar
Piwek, P. (2002) Requirements Definition, Verification, Validation and Evaluation of the CLIME Interface and Natural Language Processing Technology. ITRI Technical Report ITRI-02-03, University of Brighton.Google Scholar
Piwek, P., Evans, R. & Power, R. (1999) Editing Speech Acts: A Practical Approach to Human-Machine Dialogue. Proceedings of AMSTELOGUE '99: Workshop on the Semantics and Pragmatics of Dialogue. University of Amsterdam.Google Scholar
Piwek, P., Evans, R., Cahill, L. & Tipper, N. (2000) Natural Language Generation in the MILE System. Proceedings of the IMPACTS in NLG Workshop, Schloss Dagstuhl, Germany, pp. 3342.Google Scholar
Power, R., Scott, D. & Evans, R. (1998) What You See Is What You Meant: direct knowledge editing with natural language feedback. ECAI-98. 13th European Conference on AI, Wiley, Chichester, UK, pp. 677681.Google Scholar
Reape, M. & Mellish, C. (1999) Just what is aggregation anyway? Proceedings of the 7th European Workshop on Natural Language Generation, Toulouse, France, ACL.Google Scholar
Reiter, E. & Dale, R. (2000) Building Natural Language Generation Systems. Cambridge University Press.Google Scholar
Valente, A. (1995) Legal Knowledge Engineering: A modelling approach. IOS Press.Google Scholar
Voorhees, E. (2001) The TREC question answering track. Natural Language Engineering 7 (4): 361378.Google Scholar
Voorhees, E. (ed.) (2004) Proceedings of the Twelfth Text REtrieval Conference (TREC 2003). U.S. National Institute of Standards and Technology (NIST), NIST Special Publication 500-255, Gaithersburg, MD.Google Scholar
Winkels, R. G. F., Breuker, J. A., Boer, A. & Bosscher, D. (1999) Intelligent Information Serving For The Legal Practitioner. Law and Technology (LawTech-99). IASTED, ACTA Press, Calgary (CA), pp. 6470.Google Scholar
Winkels, R. G. F., Boer, A. & Hoekstra, R. (2002) CLIME: Lessons Learned in Legal Information Serving. In: van Harmelen, F. (ed.), Proceedings of the 15th European Conference on Artificial Intelligence (ECAI-2002). IOS Press.Google Scholar