A convolution BiLSTM neural network model for Chinese event extraction

Zeng, Ying and Yang, Honghui and Feng, Yansong and Wang, Zheng and Zhao, Dongyan (2016) A convolution BiLSTM neural network model for Chinese event extraction. In: Natural Language Understanding and Intelligent Applications : 5th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2016, and 24th International Conference on Computer Processing of Oriental Languages, ICCPOL 2016, Kunming, China,. Lecture Notes in Computer Science . Springer, Cham, pp. 275-287. ISBN 9783319504957

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Chinese event extraction is a challenging task in information extraction. Previous approaches highly depend on sophisticated feature engineering and complicated natural language processing (NLP) tools. In this paper, we first come up with the language specific issue in Chinese event extraction, and then propose a convolution bidirectional LSTM neural network that combines LSTM and CNN to capture both sentence-level and lexical information without any hand-craft features. Experiments on ACE 2005 dataset show that our approaches can achieve competitive performances in both trigger labeling and argument role labeling.

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The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-50496-4_23
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04 Jan 2017 11:52
Last Modified:
16 Jul 2024 03:58