Paper: Word Vectors And Two Kinds Of Similarity

ACL ID P06-2110
Title Word Vectors And Two Kinds Of Similarity
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

This paper examines what kind of similar- ity between words can be represented by what kind of word vectors in the vector space model. Through two experiments, three methods for constructing word vec- tors, i.e., LSA-based, cooccurrence-based and dictionary-based methods, were com- pared in terms of the ability to represent two kinds of similarity, i.e., taxonomic similarity and associative similarity. The result of the comparison was that the dictionary-based word vectors better re- flect taxonomic similarity, while the LSA- based and the cooccurrence-based word vectors better reflect associative similarity.