Markov random field based English part-of-speech tagging system
Sung-Young Jung, Young C. Park, Key‐Sun Choi, Youngwhan Kim · 1996
Probabilistic models have been widely used for natural language processing. Part-of-speech tagging, which assingns the most likely tag to each word in a given sentence, is one of the problems which can be solved by statistical approach. Many researchers have 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 problem, and adapting to new environments. In this paper, we propose a Markov radom field (MRF) model based approach to the tagging problem. The MRF provides the base frame to combine various statistical information with maximum entropy (ME) method. As Gibbs distribution can be used to describe a posteriori probability of tagging, we use it in maximum a posteriori (MAP) estimation of optimizing process. 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 HMM based tagging in data sparseness problem.