Paper: A Linear Least Squares Fit Mapping Method For Information Retrieval From Natural Language Texts

ACL ID C92-2069
Title A Linear Least Squares Fit Mapping Method For Information Retrieval From Natural Language Texts
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1992
Authors

This paper describes a unique method for mapping nat- ural language texts to canonical terms that identify the contents of the texts. This method learns empirical as- sociations between free-form texts and canonical terms from human-assigned matches and determines a Lin- ear Least Squares Fit (LLSF) mapping function which represents weighted connections between words in the texts and the canonical terms. The mapping function enables us to project an arbitrary text to the canon- ical term space where the "transformed" text is com- pared with the terms, and similarity scores are obtained which quantify the relevance between the the text and the terms. This approach has superior power to dis- cover synonyms or related terms and to preserve the context sensitivity of the mapping. We achieved a...