Abstract

Probabilistic models have been widely used for natural language processing. Part-of-speech tagging, which assigns the most likely tag to each word in a given sentence, is one of the problenm which can he solved by statistical apt) roach. Many researchers ha.w tried to solve the problem by hidden Markov model (HMM), which is well known as one of the statistical models. But it has many difficulties: integrating heterogeneous information, coping with data sparseness prohlenL and adapting t.o new environments. In this paper, we pro pose a Markov tadore field (MRF) model based apl)roach to the tagging problem. The MRF provides the base [kame to combine various statistical information with maximum entropy (ME) method. As Gibbs distribution can be used to describe a posterJori probability of tag- ging, we use it in maximum a postcriori (MAP) estimation of optimizing pro- cess. Besides, several tagging models are developed to show the effect of adding information. Experimental results show that the performance of the tagger gets improved as we add more statistical information, and that MRF-based tagging model is better than ttMM based tagging model in data sparseness problem.

Citations