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

摘要：

　　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 po-sition 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.

关键词： Chinese word segmentation Deep learning Convolutional neural networks

作者: Zhipeng Xie

作者单位: School of Computer ScienceFudan University,Shanghai,China

会议类型: 国内会议

会议名称: 第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会

会议地点: 南京

会议语种:英文

页码: 1-12

在线出版日期: 2017-10-13（万方平台首次上网日期，不代表论文的发表时间）