Lexical Concept Acquisition from Collocation Map

Lexical Concept Acquisition From Collocation Map 1 Young S. Han, Young Kyoon Han, and Key-Sun Choi Computer Science Department Korea Advanced Institute of Science and Technology Taejon, 305-701, Korea yshan@csking.kaist.ac.kr, kschoi@csking.kaist.ac.kr Abstract This paper introduces an algorithm for automatically acquiring the conceptual struc- ture of each word from corpus. The concept of a word is defined within the proba- bilistic 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. 1 Introduction The level of the conceptual representation of words can be very complex in certain con- texts, but in this paper we assume rather simple structure in which a concept is a set of weighted associated words. We propose an automatic concept acquisition framework based on the conditional probabilities suppliedd by a network representation of lexical re- lations. The network is in the spirit of Belief Net, but the probabilities are not necessarily Bayesian. In fact this variation of Bayesian Net is discussed recently by (Neal, 1992). We employed the Belief Net with non Bayesian probabilities as a base for representing the statistical relations among concepts, and implemented the details of the computation. Belief or Bayesian Nets have been extensively studied "in the normative expert systems (Heckerman, 1991). Experts provided the network with the Bayesian(subjective) proba- bilities solely based on his/her technical experiences. Thus the net has been also known as a Belief Net among a dozen other names that share all or some of the principles of Bayesian net. The probabilistic model has been also used in the problems of integrating various sources of evidences within sound framework (Cho, 1992). One of the powerful features of Belief Net is that the conditional independences of the variables in the model are naturally captured, on which we can derive a form of probabilistic inference. If we regard the occurrence of a word as a model variable and assume the variables occur within some conditional influences of the variables(words) that previously took place, the Belief approach appears to be appropriate to compute some aspects of lexical relations latent in 1 This work was supported in part by a grant from Korea National Science Foundation as a basic research project and by a grant from Korea Ministry of Science and Technology in project "an intelligent multimedia information …

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