A Connectionist and Symbolic Hybrid for Improving Legal Research

The task of legal research is complex, requiring an understanding of multiple interpretations of legal language, an awareness of the relationships between judicial decisions, and an ability to use analogical reasoning to find relevant documents. Attempts to automate parts of the process with computers have met with limited success. We describe a system called SCALIR which attempts to remedy these problems by combining connectionist and symbolic artificial intelligence approaches. This hybrid representational scheme gives SCALIR the ability to make both associative and deductive inferences. The system also provides an alternative to the traditional view of computer-assisted legal research by using a direct-manipulation style, interface, making searches into an interactive process, and by employing user feedback to improve its performance over time. In addition, we suggest a further application of SCALIR to a part of the analogy task, and argue that this approach is complementary with case-based reasoning techniques.

[1]  William S. Cooper,et al.  A definition of relevance for information retrieval , 1971, Inf. Storage Retr..

[2]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[3]  Ronald J. Brachman,et al.  What IS-A Is and Isn't: An Analysis of Taxonomic Links in Semantic Networks , 1983, Computer.

[4]  Karl Branting.  Representing and reusing explanations of legal precedents , 1989, ICAIL '89.

[5]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: I. An account of basic findings. , 1981 .

[6]  Gerard Salton,et al.  Parallel text search methods , 1988, CACM.

[7]  Daniel E. Rose,et al.  Legal information retrieval a hybrid approach , 1989, ICAIL '89.

[8]  Deborah L. McGuinness,et al.  Knowledge representation, connectionism and conceptual retrieval , 1988, SIGIR '88.

[9]  L. Thorne McCarty,et al.  Permissions and Obligations , 1983, IJCAI.

[10]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[11]  W. Freeman.  Second Commentary: On the proper treatment of connectionism by Paul Smolensky (1988) - Neuromachismo Rekindled , 1989 .

[12]  Craig Stanfill,et al.  Parallel free-text search on the connection machine system , 1986, CACM.

[13]  John H. Holland,et al.  Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .

[14]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some tests and extensions of the model. , 1982, Psychological review.

[15]  Michael C. Mozer,et al.  Inductive Information Retrieval Using Parallel Distributed Computation. , 1984 .

[16]  L. Thorne McCarty,et al.  Reflections on "Taxman": An Experiment in Artificial Intelligence and Legal Reasoning , 1977 .

[17]  Carole D. Hafner,et al.  Obstacles to the development of logic-based models of legal reasoning , 1988 .

[18]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[19]  P. Smolensky.  On the proper treatment of connectionism , 1988, Behavioral and Brain Sciences.

[20]  Marvin Minsky,et al.  Steps toward Artificial Intelligence , 1995, Proceedings of the IRE.

[21]  James A. Reggia,et al.  Connectionist models and information retrieval , 1990 .

[22]  Michael D. Gordon.  Probabilistic and genetic algorithms in document retrieval , 1988, CACM.

[23]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[24]  Moshé M. Zloof.  Query-by-Example: A Data Base Language , 1977, IBM Syst. J..

[25]  James A. Hendler,et al.  Marker‐passing over Microfeatures: Towards a Hybrid Symbolic/Connectionist Model , 1989 .

[26]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[27]  Arthur L. Samuel,et al.  Some studies in machine learning using the game of checkers" in computers and thought eds , 1995 .

[28]  Allen Newell,et al.  Physical Symbol Systems , 1980, Cogn. Sci..

[29]  Kevin D. Ashley,et al.  A case-based system for trade secrets law , 1987, ICAIL '87.

[30]  Richard K. Belew,et al.  A connectionist approach to conceptual information retrieval , 1987, ICAIL '87.

[31]  Kui-Lam Kwok.  A neural network for probabilistic information retrieval , 1989, SIGIR '89.

[32]  Layman E. Allen,et al.  Some problems in designing expert systems to aid legal reasoning , 1987, ICAIL '87.

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

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