Lexical Concept Acquisition From Collocation Map

This paper introduces an algorithm for automatically acquiring the conceptual structure of each word from corpus. The concept of a word is defined within the probabilistic framework. A variation of Belief Net named as Collocation Map is used to compute the probabilities. The Belief Net captures the conditional independences of words, which is obtained from the cooccurrence relations. The computation in general Belief Nets is known to be NP-hard, so we adopted Gibbs sampling for the approximation of the probabilities. The use of Belief Net to model the lexical meaning is unique in that the network is larger than expected in most other applications, and this changes the attitude toward the use of Belief Net. The lexical concept obtained from the Collocation Map best reflects the subdomain of language usage. The potential application of conditional probabilities the Collocation Map provides may extend to cover very diverse areas of language processing such as sense disambiguation, thesaurus construction, automatic indexing, and document classification.