First steps in building a model for the retrieval of court decisions

International Journal of Human Computer Studies

Volumn 57, Issue 5, 2002, Pages 429-446

  Moens, Marie Francine ^a   De Busser, Rik ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Artificial intelligence; Information retrieval; Legal documents

Indexed keywords

ARTIFICIAL INTELLIGENCE; CD-ROM; INFORMATION RETRIEVAL; LAWS AND LEGISLATION; MULTIMEDIA SYSTEMS; QUERY LANGUAGES; XML;

LEGAL DOCUMENTS;

HUMAN COMPUTER INTERACTION;

EID: 0036865520     PISSN: 10715819     EISSN: None     Source Type: Journal    
DOI: 10.1006/ijhc.2002.1029     Document Type: Article

References (44)

1. Allen, J. (1995). Natural Language Understanding (2nd edn). Redwood City, CA: Benjamin/Cummings.
2. Ashley, K.D. (1990). Modeling Legal Argument: Reasoning with Cases and Hypotheticals. Cambridge, MA: The MIT Press.
3. Ashley, K.D. (1992). Case-based reasoning and its implications for legal expert systems. Artificial Intelligence and Law, 1, 113-208.
4. Basili, R., Pazienza, M.T., & Vindigni, M. (2000). Corpus-driven learning of event recognition rules. Proceedings on Machine Learning for Information Extraction, Humboldt University, Berlin, 21 August 2000.
5. Bing, J. (1987). Performance of legal text retrieval systems: the curse of Boole. Law Library Journal, 79, 187-202.
6. Branting, L.K. (2000). Reasoning with Rules and Precedents: A Computational Model of Legal Analysis. Boston: Kluwer Academic Publishers.
7. Brümnghaus, S., & Ashley, K.D. (1997). Finding factors: learning to classify case opinions under abstract fact categories. Proceedings of the 6th International Conference on Artificial Intelligence and Law, pp. 123-131. New York: ACM.
8. Brüninghaus, S., & Ashley, K.D. (1999). Toward adding knowledge to learning algorithms for indexing legal cases. Proceedings of the 7th International Conference on Artificial Intelligence and Law, pp. 9-17. New York: ACM.
9. Brüninghaus, S., & Ashley, K.D. (2001). The role of information extraction for textual CBR. In D.W. Aha, I. Watson & Q. Yang, Eds. Case-based Reasoning Research and Development: Proceedings of the 4th International Conference on Case-based Reasoning (ICCBR-01), Vancouver, CA. Springer Lecture Notes in Artificial Intelligence, Berlin: Springer.
10. Dick, J.P. (1989). Representation of legal text for conceptual retrieval. Proceedings of the 2nd International Conference on Artificial Intelligence and Law, pp. 244-253. New York: ACM.
11. Fox, E.A., & Sornil, O. (1999). Digital libraries. In R. Baeza-Yates & B. Ribeiro-Neto, Eds. Modern Information Retrieval pp. 415-432. Harlow, UK: Addisson-Wesley.
12. Fuhr, N., & Großjohann, K. (2001). XIRQL: a query language for information retrieval in XML Documents. Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 172-180. New York: ACM.
13. Gardner, A. (1987). An Artificial Intelligence Approach to Legal Reasoning. Cambridge, MA: MIT Press.
14. Grosz, B., & Sidner, C.J. (1986). Attention, intention, and the structure of discourse. Computational Linguistics, 12, 175-204.
15. Hafner, C.D. (1981). An Information Retrieval System Based on a Computer Model of Legal Knowledge. Ann Arbor: UMI Research Press.
16. Hafner, C.D. (1987). Conceptual organization of case law knowledge bases. Proceedings of the 1st International Conference on Artificial Intelligence and Law, pp. 35-42. New York: ACM.
17. Hearst, M.A. (1997). TextTiling: segmenting text into multi-paragraph subtopic passages. Computational Linguistics, 23, 33-64.
18. Hovy, E.H. (1993). Automated discourse generation using discourse structure relations. Artificial Intelligence, 63, 341-385.
19. Hunter, D. (1999). Out of their minds: legal theory in neural networks. Artificial Intelligence and Law, 7, 129-151.
20. Kolodner, J. (1993). Case-based Reasoning. San Mateo, CA: Morgan Kaufmann.
21. Lederer, F.I. (1996). Technology augmented litigation. Proceedings of the 1st European Conference-on Law, Computers and AI Exeter, April 15-16, pp. 70-81.
22. Mann, W.C., Matthiessen, C.M.I.M. & Thompson, S.A. (1992). Rhetorical Structure Theory and text analysis. In W.C. Mann & S.A. Thompson, Eds. Discourse Description: Diverse Linguistic Analyses of a Fund-raising Text, pp. 39-78. Amsterdam: John Benjamins.
23. Mann, W.C. & Thompson, S.A. (1988). Rhetorical Structure Theory: toward a functional theory of text organization. Text, 8, 243-281.
24. Marcu, D. (1997). The rhetorical parsing, summarization, and generation of natural language text. Ph.D. Thesis, University of Toronto, Canada.
25. Marcu, D. (2000). The Theory and Practice of Discourse Parsing and Summarization. Cambridge, MA: The MIT Press.
26. Matthijssen, L. (2000). Jurisprudentiedatabanken. Een internationaal vergelijkende studie naar de publicatie van rechterlijke uitspraken met behulp van informatietechnoloqie (IteR 34). Deventer: Kluwer.
27. McCarty, L.T. (1984). Intelligent legal information systems: problems and prospects. In C. Campbell, Ed. Data Processing and the Law, pp. 125-151. London: Sweet & Maxwell.
28. Moens, M.-F. (2001). Innovative techniques for legal text retrieval. Artificial Intelligence and Law, 9, 29-57.
29. Moens, M.-F., & De Busser, R. (2001). Generic topic segmentation of document texts. Proceedings of the 24th ACM SIGIR Annual International Conference on Research and Development in Information Retrieval, pp. 418-419. New York: ACM.
30. Moens, M.-F., & Uyttendaele, C. (1997). Automatic structuring and categorization as a first step in summarizing legal cases. Information Processing and Management, 33, 727-737.
31. Moens, M.-F., Uyttendaele, C., & Dumortier, J. (1999a). Information extraction from legal texts: the potential of discourse analysis. International Journal of Human-Computer Studies, 51, 1155-1171.
32. Moens, M.-F., Uyttendaele, C., & Dumortier, J. (1999b). Abstracting of legal cases: the potential of clustering based on the selection of representative objects. Journal of the American Society for Information Science, 50, 151-161.
33. Moore, J.D., & Paris, C.L. (1993). Planning texts for advisory dialogues: capturing intentional and rhetorical information. Computational Linguistics, 19, 651-695.
34. Moore, J.D., & Pollack, M.E. (1992). A problem for RST: the need for multi-level discourse analysis. Computational Linguistics, 18, 537-544.
35. Rissland, E., Skalak, D., & Friedman, M. (1996). BankXX: supporting legal arguments through heuristic retrieval. Artificial Intelligence and Law, 4, 1-71.
36. Salton, G., Allan, J., Buckley, C., & Singhal, A. (1994). Automatic analysis, theme generation, and summarization of machine-readable texts. Science, 264, 1421-1426.
37. Soderland, S. (1999). Learning information extraction rules for semi-structured and free text. Machine Learning, 34, 233-272.
38. Thompson, P. (2001). Automatic categorization of case law. Proceedings of the 8th International Conference on Artificial Intelligence and Law, pp. 70-77. New York: ACM.
39. Uyttendaele, C., Moens, M.-F. & Dumortier, J. (1998). SALOMON: abstracting of legal cases for effective access to court decisions. Artificial Intelligence and Law, 6, 59-79.
40. Van Belle, W. (1990). Van tekstanalyse tot tekstplanning: de Rhetorical Structure Theory. Preprint, Department of Linguistics, Katholieke Universiteit Leuven, Belgium.
41. Van Dijk, T.A. (1997). The study of discourse. In T.A. Van Dijk, Ed. Discourse as Structure and Process (Discourse Studies: A Multidisciplinary Introduction 1), pp. 1-34. London: Sage.
42. Weber, R. (1999). Intelligent jurisprudence research: a new concept. Proceedings of the 7th International Conference on Artificial Intelligence and Law, pp. 164-172. New York: ACM.
43. Winkels, R., Bosscher, D., Boer, A., & Hoekstra, R. (2000). Extended conceptual retrieval. In J. Breuker, R. Leenes & R. Winkels, Eds. Legal Knowledge and Information Systems, pp. 85-97. Amsterdam: IOS Press.
44. Zeleznikow, J., & Stranieri, A. (1995). The Split-Up system: integrating neural networks and rule-based reasoning in the legal domain. Proceedings of the 5th International Conference on Artificial Intelligence and Law, pp. 185-194. New York: ACM.