On modeling of information retrieval concepts in vector spaces
ACM Transactions on Database Systems (TODS)
A failure analysis of the limitation of suffixing in an online environment
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
Conceptual information retrieval using RUBRIC
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
RUBRIC: an environment for full text information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic Indexing: An Experimental Inquiry
Journal of the ACM (JACM)
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Beyond boolean search: FLEXICON, a legal tex-based intelligent system
ICAIL '91 Proceedings of the 3rd international conference on Artificial intelligence and law
FLEXICON: an evaluation of a statistical ranking model adapted to intelligent legal text management
ICAIL '93 Proceedings of the 4th international conference on Artificial intelligence and law
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RUBRIC is an expert system for full-text information retrieval. The underlying model for RUBRIC's information retrieval process is based upon fuzzy set theory. The RUBRIC developers have compared RUBRIC to the boolean retrieval model, which it subsumes. This study compares RUBRIC to the Vector Space Model for information retrieval, using RUBRIC's own test collection of thirty news articles from the Reuters News Service and their test search for articles which satisfy the information need to find out about "violent acts of terrorism." Results indicate that the vector space model is comparable to RUBRIC for relevant documents, while RUBRIC performs better at retrieving marginally relevant documents.