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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Acknowledgments
The research is supported by National Natural Science Foundation of China (Contract 61202216). Liu is partially supported by the Science Foundation Ireland (Grant 12/CE/I2267 and 13/RC/2106) as part of the ADAPT Centre at Dublin City University. We sincerely thank the anonymous reviewers for their thorough reviewing and valuable suggestions.
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Zhang, J., Meng, F., Wang, M., Zheng, D., Jiang, W., Liu, Q. (2016). Is Local Window Essential for Neural Network Based Chinese Word Segmentation?. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_37
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DOI: https://doi.org/10.1007/978-3-319-47674-2_37
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