基于高层语义注意力机制的中文实体关系抽取

doi:10.16088/j.issn.1001-6600.2019.01.004

Abstract

Abstract: Relation extraction is an important part of many information extraction systems that mines structured facts from texts. Recently, deep learning has achieved good results in relation extraction. Attention mechanism is also gradually applied to networks, which improves the performance of the task. However, the current attention mechanism is mainly applied to the basic features on the lexical level rather than the higher overall features. In order to obtain more information of high-level features for relation predicting, this paper proposes high-level semantic attention-based piecewise convolutional neural networks (PCNN_HSATT), which adds an attention layer after the piecewise max pooling layer in order to get significant information of sentence global features. Furthermore, this paper puts forward a data augmentation method by utilizing an external dictionary HIT IR-Lab Tongyici Cilin (Extended) to reply the sparse challenge in Chinese entity relation extraction corpus. Experimental results on COAE2016 and ACE2005 Chinese datasets are 78.41% and 73.94% respectively. Compared with the best existing method SVM, the results improve 10.45% and 0.67% respectively, which demonstrates that this approach is effective in Chinese entity relation extraction task.

Key words: relation extraction, convolutional neural networks, attention mechanism, data augmentation, dependency syntax constraint

CLC Number:

TP391

WU Wenya,CHEN Yufeng,XU Jin’an,ZHANG Yujie. High-level Semantic Attention-based Convolutional Neural Networks for Chinese Relation Extraction[J].Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(1): 32-41.

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High-level Semantic Attention-based Convolutional Neural Networks for Chinese Relation Extraction

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