Chinese Word Segmentation with Conditional Support Vector Inspired Markov Models

In this paper, we present the proposed method of participating SIGHAN-2010 Chinese word segmentation bake-off. In this year, our focus aims to quick train and test the given data. Unlike the most structural learning algorithms, such as conditional random fields, we design an in-house development conditional support vector Markov model (CMM) framework. The method is very quick to train and also show better performance in accuracy than CRF. To give a fair comparison, we compare our method to CRF with three additional tasks, namely, CoNLL-2000 chunking, SIGHAN-3 Chinese word segmentation. The results were encourage and indicated that the proposed CMM produces better not only accuracy but also training time efficiency. The official results in SIGHAN-2010 also demonstrates that our method perform very well in traditional Chinese with fine-tuned features set.

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