Closed-Set Chinese Word Segmentation Based on Convolutional Neural Network Model

This paper proposes a neural model for closed-set Chinese word segmentation. The model follows the character-based approach which assigns a class label to each character, indicating its relative position within the word it belongs to. To do so, it first constructs shallow representations of characters by fusing unigram and bigram information in limited context window via an element-wise maximum operator, and then build up deep representations from wider contextual information with a deep convolutional network. Experimental results have shown that our method achieves better closed-set performance compared with several state-of-the-art systems.