On Legal Texts and Cases

The search employed by judicial professionals when seeking for past similar legal decisions is known as jurisprudence research. Humans employ analogical reasoning when comparing a given actual situation with past decisions, noting the affinities between them. In the process of being reminded of a similar situation when faced to a new one, Case-Based Reasoning (CBR) systems simulate analogical reasoning. Judicial professionals have two sources of jurisprudence research: books and database systems. The search in books is time-consuming and imprecise due to the limitations of humans’ memory. Available text database systems do not guarantee the retrieval of useful documents. PRUDENTIA is the case-based reasoner tailored to the Brazilian system that confers efficiency to jurisprudence research. Judicial cases are described with natural language text, comprising a collection of textual documents. These texts are the experiences that require case engineering to be modeled in a structured representation of cases. We have developed an automatic means of performing the case engineering, that is, converting legal texts into structured representation of cases. Examples of PRUDENTIA demonstrate the power of similarity-based retrieval in a textual CBR system against text database applications improving the usefulness of the documents retrieved.

[1]  Alan F. Smeaton.  Information Retrieval: Still Butting Heads with Natural Language Processing? , 1997, SCIE.

[2]  Edwina L. Rissland,et al.  A case-based approach to intelligent information retrieval , 1995, SIGIR '95.

[3]  Kevin D. Ashley.  Modeling legal argument - reasoning with cases and hypotheticals , 1991, Artificial intelligence and legal reasoning.

[4]  Udo Hahn,et al.  Deep Knowledge Discovery from Natural Language Texts , 1997, KDD.

[5]  M. E. Maron,et al.  An evaluation of retrieval effectiveness for a full-text document-retrieval system , 1985, CACM.

[6]  James C. Lester,et al.  Justification Structures for Document Reuse , 1996, EWCBR.

[7]  Ellen Riloff,et al.  Classifying Texts Using Relevancy Signatures , 1992, AAAI.

[8]  Beth Sundheim,et al.  A Performance Evaluation of Text-Analysis Technologies , 1991, AI Mag..

[9]  K. Branting,et al.  Building Explanations from Rules and Structured Cases , 1991, Int. J. Man Mach. Stud..

[10]  Daphne Gelbart,et al.  Building expert systems in case-based law , 1992 .

[11]  Daphne Gelbart,et al.  FLEXICON: an evaluation of a statistical ranking model adapted to intelligent legal text management , 1993, ICAIL '93.

[12]  A. Valente,et al.  Legal Knowledge Engineering - A Modelling Approach , 1995 .

[13]  Gerard Salton,et al.  Dynamic information and library processing , 1975 .

[14]  Alejandro Martins,et al.  A Large Case-Based Reasoner for Legal Cases , 1997, ICCBR.

[15]  W. Bruce Croft,et al.  Inference networks for document retrieval , 1989, SIGIR '90.

[16]  Ellen Riloff,et al.  Information extraction as a basis for high-precision text classification , 1994, TOIS.

[17]  J. C. Smith,et al.  The Application of Expert Systems Technology to Case-Based Law , 1987, ICAIL.

[18]  J. C. Smith.  The use of lexicons in information retrieval in legal databases , 1997, ICAIL '97.

[19]  Gobinda G. Chowdhury,et al.  Automatic extraction of citations from the text of English-language patents - an example of template mining , 1996, J. Inf. Sci..

[20]  Alejandro Martins,et al.  Reusing Cases to the Automatic Index Assignment from Textual Documents , 1998 .

[21]  M. E. Maron,et al.  Full-text information retrieval: Further analysis and clarification , 1990, Inf. Process. Manag..

[22]  David C. Blair.  STAIRS redux: thoughts on the STAIRS evaluation, ten years after , 1996 .