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 ...
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.
Overview