基于CNN和双向LSTM融合的实体关系抽取

张晓斌; 陈福才; 黄瑞阳

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

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基于CNN和双向LSTM融合的实体关系抽取

论文 | 更新时间：2024-06-03

- 基于CNN和双向LSTM融合的实体关系抽取
- Relation extraction based on CNN and Bi-LSTM
- 网络与信息安全学报 2018年4卷第9期页码：44-51
- 作者机构：
- 作者简介：
  
  [ "张晓斌（1993-），男，福建漳州人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为文本挖掘、实体关系抽取。" ]
  [ "陈福才（1974-），男，江西高安人，硕士，国家数字交换系统工程技术研究中心研究员、硕士生导师，主要研究方向为大数据分析与处理。" ]
  [ "黄瑞阳（1986-），男，福建漳州人，国家数字交换系统工程技术研究中心助理研究员，主要研究方向为文本挖掘、信息抽取。" ]
- 基金信息：
  
  国家自然科学基金创新研究群体基金资助项目;The Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61521003);国家重点研发计划项目;The National Key R＆D Program of China(2016YFB0800101)
- DOI：10.11959/j.issn.2096-109x.2018074
  中图分类号： TP393
- 网络首发：2018-09，
  
  纸质出版：2018-09-15
- 稿件说明：
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张晓斌, 陈福才, 黄瑞阳. 基于CNN和双向LSTM融合的实体关系抽取[J]. 网络与信息安全学报, 2018,4(9):44-51.

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.
张晓斌, 陈福才, 黄瑞阳. 基于CNN和双向LSTM融合的实体关系抽取[J]. 网络与信息安全学报, 2018,4(9):44-51. DOI： 10.11959/j.issn.2096-109x.2018074.

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.

关键词

Keywords

references

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