Abstract:

 The performance of Chinese word segmentation has been greatly improved by word-position-based approaches in recent years. This approach treats Chinese word segmentation as a wordposition tagging problem. With the help of powerful sequence tagging model, word-position-based method quickly rose as a mainstream technique in this field. Feature template selection is crucial in this method. We further studied this technique via using four wordpositions and conditional random fields. Closed evaluations are performed on corpus from the third and the fourth international Chinese word segmentation Bakeoff, and comparative experiments are performed on different feature templates. Experimental results show that the feature template set: TMPT-10′  is much better performance than the traditional template set.
 

Key words: Chinese word segmentation, conditional random fields, word-position tagging, feature template