Abstract
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.
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