Lexical Concept Acquisition from Collocation Map
Young Soo Han, Young Kyoon Han, Key‐Sun Choi · 1993
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 1opted Gibbs sampling for the approximation of the probabilities.