A Study on Word Similarity Using Context Vector Models

There is a need to measure word similarity when processing natural languages, especially when using generalization, classification, or example -based approaches. Usually, measures of similarity between two words are defined according to the distance between their semantic classes in a semantic taxonomy . The taxonomy approaches are more or less semantic -based that do not consider syntactic similarit ies. However, in real applications, both semantic and syntactic similarities are required and weighted differently. Word similarity based on context vectors is a mixture of syntactic and semantic similarit ies. In this paper, we propose using only syntactic related co-occurrences as context vectors and adopt information theoretic models to solve the problems of data sparseness and characteristic precision. The probabilistic distribution of co-occurrence context features is derived by parsing the contextual environment of each word , and all the context features are adjusted according to their IDF (inverse document frequency) values. The agglomerative clustering algorithm is applied to group similar words according to their similarity values . It turns out that words with similar syntactic categories and semantic classes are grouped together.