Abstract
Recent studies have shown effectiveness in using neural networks for Chinese Word Segmentation (CWS). However, these models, constrained by the domain and size of the training corpus, do not work well in domain adaptation. In this paper, we propose a novel instance-transferring method, which use valuable target domain annotated instances to improve CWS on different domains. Specifically, we introduce semantic similarity computation based on character-based n-gram embedding to select instances. Furthermore, training sentences similar to instances are used to help annotate instances. Experimental results show that our method can effectively boost cross-domain segmentation performance. We achieve state-of-the-art results on Internet literatures datasets, and competitive results to the best reported on micro-blog datasets.
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Acknowledgments
The authors are supported by National Nature Science Foundation of China (Contract 61370130 and 61473294), and the Fundamental Research Funds for the Central Universities(2015JBM033), and International Science and Technology Cooperation Program of China under grant No. 2014DFA11350.
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Zhang, Y., Xu, J., Miao, G., Chen, Y., Zhang, Y. (2018). Addressing Domain Adaptation for Chinese Word Segmentation with Instances-Based Transfer Learning. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_3
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