Identifying concept attributes using a classifier

Authors:
Massimo Poesio;Abdulrahman Almuhareb
Affiliations:
University of Essex;University of Essex
Venue:
DeepLA '05 Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition
Year:
2005

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Abstract

We developed a novel classification of concept attributes and two supervised classifiers using this classification to identify concept attributes from candidate attributes extracted from the Web. Our binary (attribute / non-attribute) classifier achieves an accuracy of 81.82% whereas our 5-way classifier achieves 80.35%.