Is Local Window Essential for Neural Network based Chinese Word Segmentation?

摘要：

　　Neural network based Chinese Word Segmentation(CWS)approaches can bypass the burdensome feature engineering comparing with the conventional ones.All previous neural network based approaches rely on a local window in character sequence labelling process.It can hardly exploit the outer context and may preserve indifferent inner context.Moreover,the size of local window is a toilsome manual-tuned hyper-parameter that has significant influence on model performance.We are wondering if the local window can be discarded in neural network based CWS.In this paper,we present a window-free Bi-directional Long Short-term Memory(Bi-LSTM)neural network based Chinese word segmentation model.The model takes the whole sentence under consideration to generate reasonable word sequence.The experiments show that the Bi-LSTM can learn sufficient context for CWS without the local window.

关键词： Chinese Word Segmentation Neural Network Window

作者: Jinchao Zhang Fandong Meng Mingxuan Wang Daqi Zheng Wenbin Jiang Qun Liu

作者单位: Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences

会议类型: 国内会议

会议名称: 第十五届全国计算语言学学术会议(CCL2016)暨第四届基于自然标注大数据的自然语言处理国际学术研讨会(NLP-NABD-2016)

会议地点: 烟台

会议语种:英文

页码: 1-8

在线出版日期: 2016-10-14（万方平台首次上网日期，不代表论文的发表时间）