Word Vectors and Two Kinds of Similarity 
 
 This paper examines what kind of similarity between words can be represented by what kind of word vectors in the vector space model . Through two experiments, three methods for constructing word vectors , i.e., LSA-based, cooccurrence-based and dictionary-based methods , were compared 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 reflect taxonomic similarity , while the LSA-based and the cooccurrence-based word vectors better reflect associative similarity .  