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2018 | OriginalPaper | Chapter

Attention-Based Convolutional Neural Networks for Chinese Relation Extraction

Authors : Wenya Wu, Yufeng Chen, Jinan Xu, Yujie Zhang

Published in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Publisher: Springer International Publishing

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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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previous chapter An End-to-End Entity and Relation Extraction Network with Multi-head Attention
next chapter A Study on Improving End-to-End Neural Coreference Resolution
Footnotes
1
We use Stanford Parser to perform dependency parsing on sentences.
 
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Metadata
Title
Attention-Based Convolutional Neural Networks for Chinese Relation Extraction
Authors
Wenya Wu
Yufeng Chen
Jinan Xu
Yujie Zhang
Copyright Year
2018
Book
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-030-01715-6

Electronic ISBN: 978-3-030-01716-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01716-3_13

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