Abstract
This paper aims to perform the task of Chinese Word Segmentation on judgements. For this task, the main challenge is the lack of the annotated corpus. To alleviate this challenge, this paper proposes an active learning approach. Specifically, on the basis of a few initial annotated samples, a new active learning approach is proposed to annotate some informative characters, and then select the context around these characters for annotation. In the active learning approach, it not only considers the uncertainty of the sample, but also leverages the redundancy of the sample for the selection of informative characters. Furthermore, this paper adopts the local annotation strategy, which select a substrings around the informative characters rather than the whole sentences and thus could also reduce the annotation. The empirical study demonstrates that the proposed approach effectively reduces the annotation cost and performances better than other baseline sample selection strategies under the same scale of annotation.
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Acknowledgments
This research work has been partially supported by three NSFC grants, No. 61375073, No. 61672366 and No. 61331011.
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Yan, Q., Wang, L., Li, S., Liu, H., Zhou, G. (2018). Active Learning for Chinese Word Segmentation on Judgements. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_73
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DOI: https://doi.org/10.1007/978-3-319-73618-1_73
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