A Symbolic and Connectionist Approach to Legal Information Retrieval
Many existing information retrieval (IR) systems are surprisingly ineffective at finding documents relevant to particular topics. Traditional systems are extremely brittle, failing to retrieve relevant documents unless the user's exact search string is found. They support only the most primitive trial-and-error interaction with their users and are also static. Even systems with so-called "relevance feedback" are incapable of learning from experience with users. SCALIR (a Symbolic and Connectionist Approach to Legal Information Retrieval) -- a system for assisting research on copyright law -- has been designed to address these problems. By using a hybrid of symbolic and connectionist artificial intelligence techniques, SCALIR develops a conceptual representation of document relationships without explicit knowledge engineering. SCALIR's direct manipulation interface encourages users to browse through the space of documents. It then uses these browsing patterns to improve its performance by modifying its representation, resulting in a communal repository of expertise for all of its users.
SCALIR's representational scheme also mirrors the hybrid nature of the Anglo-American legal system. While certain legal concepts are precise and rule-like, others -- which legal scholars call "open-textured" -- are subject to interpretation. The meaning of legal text is established through the parallel and distributed precedence-based judicial appeal system. SCALIR represents documents and terms as nodes in a network, capturing the duality of the legal system by using symbolic (semantic network) and connectionist links. The former correspond to a priori knowledge such as the fact that one case overturned another on appeal. The latter correspond to statistical inferences such as the relevance of a term describing a case. SCALIR's text corpus includes all federal cases on copyright law.
The hybrid representation also suggests a way to resolve the apparent incompatibility between the two prominent paradigms in artificial intelligence, the "classical" symbol-manipulation approach and the neurally-inspired connectionist approach. Part of the book focuses on a characterization of the two paradigms and an investigation of when and how -- as in the legal research domain -- they can be effectively combined.
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Humans Computers and Finding Information
Knowledge Representation Meaning and Text in AI
Approaches to Information Retrieval
Some Perspectives on the Law and Legal Research
The Structure of SCALIR
The Retrieval Process
Feedback and Learning
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active nodes actual adaptive algorithm allow approach associations behavior Boolean browsing CALR citation cited clamping component concept connectionism connectionist network copyright law corresponding court database decisions discussed displayed domain evaluation example experience fact Furthermore GOFAI headnotes hybrid system idea information retrieval initial query input interaction interface inverted file issues key number knowledge knowledge representation labels lawyers learning rule legal research link types means mechanism mouse natural language natural language processing negative feedback output parameters positive feedback problem propagation query nodes query terms question relationships relevance feedback relevant documents represent representation require response result role S-links SCALIR semantic network Shepard's Shepard's citations shown in Figure simply spreading activation statute sub-symbolic processing sub-symbolic systems supervised learning symbol grounding symbolic systems task techniques term weighting threshold traditional unclean hands user's vector weight change WESTLAW words