Transductive HMM based Chinese text chunking

In this paper. we present a novel methodology to enhance Chinese test chunking with the aid of transductive Hidden Markov Models (transductive HMMs, henceforth). We consider chunking as a special tagging problem and attempt to utilize, via a number of transformation functions, as much relevant contextual information as possible for model training. These functions enable the models to make use of contextual information to a greater extent and keep us away from costly changes of the original training and tagging process. Each of them results in an individual model with certain pros and cons. Through a number of esperiments, we succeed in integrating the best two models into a significantly better one. We carry out the chunking experiments on the HIT Chinese Treebank corpus. Experimental results show that it is an effective approach, achieving an F score of 82.38%.