Matrix computations (3rd ed.)
A Linear Least Squares Fit mapping method for information retrieval from natural language texts
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Noise reduction in a statistical approach to text categorization
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
The knowledge in multiple human relevance judgments
ACM Transactions on Information Systems (TOIS)
Computational Methods for Intelligent Information Access
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Automatic categorization of case law
Proceedings of the 8th international conference on Artificial intelligence and law
Neural Networks for Web Content Filtering
IEEE Intelligent Systems
Interact: A Staged Approach to Customer Service Automation
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Predicting risk from financial reports with regression
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
On the query reformulation technique for effective MEDLINE document retrieval
Journal of Biomedical Informatics
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This paper describes a unique example-based mapping method for document retrieval. We discovered that the knowledge about relevance among queries and documents can be used to obtain empirical connections between query terms and the canonical concepts which are used for indexing the content of documents. These connections do not depend on whether there are shared terms among the queries and documents; therefore, they are especially effective for a mapping from queries to the documents where the concepts are relevant but the terms used by article authors happen to be different from the terms of database users. We employ a Linear Least Squares Fit (LLSF) technique to compute such connections from a collection of queries and documents where the relevance is assigned by humans, and then use these connections in the retrieval of documents where the relevance is unknown. We tested this method on both retrieval and indexing with a set of MEDLINE documents which has been used by other information retrieval systems for evaluations. The effectiveness of the LLSF mapping and the significant improvement over alternative approaches was evident in the tests.