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.