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, we proposed attention-based piecewise convolutional neural networks (PCNN_ATT), which add an attention layer after the piecewise max pooling layer in order to get significant information of sentence global features. Furthermore, we put forward a data extension method by utilizing an external dictionary HIT IR-Lab Tongyici Cilin (Extended). Experiments results on ACE-2005 and COAE-2016 Chinese datasets both demonstrate that our approach outperforms most of the existing methods.
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Notes
- 1.
We use Stanford Parser to perform dependency parsing on sentences.
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Acknowledgements
The authors are supported by National Nature Science Foundation of China (Contract 61370130 and 61473294), and the Fundamental Research Funds for the Central Universities (2015JBM033), and International Science and Technology Cooperation Program of China under grant No. 2014DFA11350.
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Wu, W., Chen, Y., Xu, J., Zhang, Y. (2018). Attention-Based Convolutional Neural Networks for Chinese Relation Extraction. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_13
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