2004 | OriginalPaper | Buchkapitel
Using Fuzzy Sets in Contextual Word Similarity
verfasst von : Masrah Azmi-Murad, Trevor P. Martin
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2004
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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We propose a novel algorithm for computing asymmetric word similarity (AWS) using mass assignment based on fuzzy sets of words. Words in documents are considered similar if they appear in similar contexts. However, these similar words do not have to be synonyms, or belong to the same lexical category. We apply AWS in measuring document similarity. We evaluate the effectiveness of our method against a typical symmetric similarity measure, TF.IDF. The system has been evaluated on real world documents, and the results show that this method performs well.