Abstract
This paper describes the model we designed for the Chinese word segmentation Task of NLPCC 2015. We firstly apply a word-based perceptron algorithm to build the base segmenter. Then, we use a Bootstrap Aggregating model of bagging which improves the segmentation results consistently on the three tracks of closed, semi-open and open test. Considering the characteristics of Weibo text, we also perform rule-based adaptation before decoding. Finally, our model achieves F-score 95.12% on closed track, 95.3% on semi-open track and 96.09% on open track.
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© 2015 Springer International Publishing Switzerland
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Yu, Z., Dai, XY., Shen, S., Huang, S., Chen, J. (2015). Word Segmentation of Micro Blogs with Bagging. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_54
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DOI: https://doi.org/10.1007/978-3-319-25207-0_54
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-25207-0
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