Abstract
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. © Springer-Verlag Berlin Heidelberg 2004.
Cite
CITATION STYLE
Azmi-Murad, M., & Martin, T. P. (2004). Using fuzzy sets in contextual word similarity. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3177, 517–522. https://doi.org/10.1007/978-3-540-28651-6_76
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
