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

• 作 者: JinchaoZhang;FandongMeng;MingxuanWang;DaqiZheng;WenbinJiang;QunLiu;
• 机 构: Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Acade
• 出 处: 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
• 发表日期: 2016年期

　　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.