Paper: Markov Random Field Based English Part-Of-Speech Tagging System

ACL ID C96-1041
Title Markov Random Field Based English Part-Of-Speech Tagging System
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1996
Authors

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 tire problems which can be solved by statisticM ap- proach. Many researchers haw~ tried to solve the problem by hidden Marker model (HMM), which is well known as one of the statistical models. But it has many difficulties: integrating hetero- geneous information, coping with data sparseness prohlem, and adapting to new environments. In this paper, we pro- pose 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 tag...