Abstract
The correct definition and recognition of sentences is the basis of NLP. For the characteristics of Chinese text structure, the theory of NT clause was proposed from the perspective of micro topics. Based on this theory, this paper proposes a novel method for construction NT clause. Firstly, this paper proposes a neural network model based on Attention and LSTM (Attention-LSTM), which can identify the location of the missing Naming, and uses manually annotated corpus to train the Attention-LSTM. Secondly, in the process of constructing NT clause, the trained Attention-LSTM is used to identify the location of the missing Naming. Then the NT clause can be constructed. The accuracy of the experimental result is 81.74% (+4.5%). This paper can provide support for the task of text understanding, such as Machine Translation, Information Extraction, Man-machine Dialogue.
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Acknowledgements
This work was supported by grants from National Nature Science Foundation of China (No. 61602044), National Nature Science Foundation of China (No. 61370139), Scientific Research Project of Beijing Educational Committee (No. KM201711232022).
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Mao, T., Zhang, Y., Jiang, Y., Zhang, Y. (2018). Research on Construction Method of Chinese NT Clause Based on Attention-LSTM. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_30
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DOI: https://doi.org/10.1007/978-3-319-99501-4_30
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