Relation extraction based on CNN and Bi-LSTM

Xiaobin ZHANG; Fucai CHEN; Ruiyang HUANG

doi:10.11959/j.issn.2096-109x.2018074

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Relation extraction based on CNN and Bi-LSTM

Papers | 更新时间：2024-06-03

- Relation extraction based on CNN and Bi-LSTM
- Chinese Journal of Network and Information Security Vol. 4, Issue 9, Pages: 44-51(2018)
- 作者机构：
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- DOI：10.11959/j.issn.2096-109x.2018074
  CLC： TP393
- Online First：2018-09，
  
  Published：15 September 2018
- 稿件说明：
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Xiaobin ZHANG, Fucai CHEN, Ruiyang HUANG. Relation extraction based on CNN and Bi-LSTM[J]. Chinese Journal of Network and Information Security, 2018, 4(9): 44-51.
DOI：

Xiaobin ZHANG, Fucai CHEN, Ruiyang HUANG. Relation extraction based on CNN and Bi-LSTM[J]. Chinese Journal of Network and Information Security, 2018, 4(9): 44-51. DOI： 10.11959/j.issn.2096-109x.2018074.

摘要

实体关系抽取旨在识别网络文本中的实体，并提取出文本中实体之间隐含的关系。研究表明，深度神经网络在实体关系抽取任务上具有可行性，并优于传统关系抽取方法。目前的关系抽取方法大都使用卷积神经网络（CNN）和长短期记忆神经网络（LSTM），然而CNN只考虑连续词之间的相关性而忽略了非连续词之间的相关性。另外，LSTM虽然考虑了长距离词的相关性，但提取特征不够充分。针对这些问题，提出了一种CNN和LSTM结合的实体关系抽取方法，采用3种结合方法进行了实验，验证了该方法的有效性，在F1值上有一定的提升。

Abstract

Relation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional methods.Most of the current relation extraction methods apply convolutional neural network (CNN) and long short-term memory neural network (LSTM) methods.However

CNN just considers the correlation between consecutive words and ignores the correlation between discontinuous words.On the other side

although LSTM takes correlation between long-distance words into account

the extraction features are not sufficiently extracted.In order to solve these problems

a relation extraction method that combining CNN and LSTM was proposed.three methods were used to carry out the experiments

and confirmed the effectiveness of these methods

which had some improvement in F1 score.

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references

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