Abstract
Acquiring entity hyponymy of complex sentences can be a highly difficult process in special domain. To tackle this problem, this paper proposes a novel method that combines Bootstrapping method and Attention-Based Bidirectional Long Short-Term Memory Networks (Bo-At-BiLSTM). The experimental corpus is in the field of tourism in China. First, the bootstrapping method is used to obtain the patterns set. Then, pattern matching is employed to acquire the candidate sentences and word embedding. Next, import into the bidirectional Long Short-Term Memory Networks and introduce attention mechanism. Finally, output the results by Softmax classifier. The experimental results on the tourism corpus show that the proposed approach outperforms the baseline methods.
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Acknowledgments
This paper is supported by National key research and development plan project (Grant Nos. 2018YFC0830105, 2018YFC0830100), National Natural Science Foundation of China (Grant Nos. 61732005, 61672271, 61761026, and 61762056), Yunnan high-tech industry development project (Grant No. 201606), Natural Science Foundation of Yunnan Province (Grant No. 2018FB104), and National Natural Science Foundation of China (Grant Nos. 61562052, 61462054, and 61866019).
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Li, H., Zhang, Z., Yu, Z., Wang, H., Lai, H. (2020). Entity Hyponymy Extraction of Complex Sentence Combining Bootstrapping and At-BiLSTM in Special Domain. In: Yuan, X., Elhoseny, M. (eds) Urban Intelligence and Applications. Studies in Distributed Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-45099-1_9
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