基于短语向量和主题加权的关键词抽取方法

孙新; 盖晨; 申长虹; 张颖捷

doi:10.12263/DZXB.20200014

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基于短语向量和主题加权的关键词抽取方法

学术论文 | 更新时间：2025-12-08

- 基于短语向量和主题加权的关键词抽取方法
- The Theme-Weighted Keyphrase Extraction Algorithm Based on Phrase Embedding
- 电子学报 2021年49卷第9期页码：1682-1690
- 作者机构：
  
  1.北京理工大学计算机学院北京市海量语言信息处理与云计算应用工程技术研究中心，北京 100081
  2.北京理工大学东南信息技术研究院，福建莆田 351100
- 作者简介：
  
  [ "孙新　女， 1975年4月生于吉林省长春市.现为北京理工大学计算机学院副教授，硕士生导师. 主要研究方向为人工智能，机器学习.E-mail: sunxin@bit.edu.cn" ]
  [ "盖晨　男，1996年5月生于河北省石家庄市.现为北京理工大学计算机学院硕士生研究生.主要研究方向为人工智能.E-mail: 851774342@qq.com" ]
  [ "申长虹　女，1995年3月生于河南省.北京理工大学计算机学院硕士生.主要研究方向为人工智能. E-mail: suishelly@foxmail.com" ]
- 基金信息：
  
  国家重点研发计划项目(2017YFB0803300)
- DOI：10.12263/DZXB.20200014
  中图分类号： TP181
- 收稿：2019-12-23，
  
  修回：2020-11-24，
  
  纸质出版：2021-09-25
- 稿件说明：
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孙新,盖晨,申长虹等.基于短语向量和主题加权的关键词抽取方法[J].电子学报,2021,49(09):1682-1690.

SUN Xin,GE Chen,SHEN Chang-hong,et al.The Theme-Weighted Keyphrase Extraction Algorithm Based on Phrase Embedding[J].ACTA ELECTRONICA SINICA,2021,49(09):1682-1690.
孙新,盖晨,申长虹等.基于短语向量和主题加权的关键词抽取方法[J].电子学报,2021,49(09):1682-1690. DOI： 10.12263/DZXB.20200014.

SUN Xin,GE Chen,SHEN Chang-hong,et al.The Theme-Weighted Keyphrase Extraction Algorithm Based on Phrase Embedding[J].ACTA ELECTRONICA SINICA,2021,49(09):1682-1690. DOI： 10.12263/DZXB.20200014.

摘要

现有关键词抽取算法缺乏对短语的有效表示，为抽取出更能反映文本主题的关键短语，本文提出一种基于短语向量的关键词抽取方法PhraseVecRank.首先设计基于LSTM（Long Short-Term Memory）和CNN（Convolutional Neural Network）自编码器的短语向量构建模型，解决复杂短语的语义表示问题.然后，利用短语向量对每个候选短语计算主题权重，通过主题加权排序提高关键词抽取的效果.在公共数据集和学术论文数据上的实验表明，本文提出的方法能够有效提取与文本主题信息相关的关键短语，同时利用自编码器构造的短语向量可以更好地表示短语的语义信息.

Abstract

Keyword extraction is a key basic problem in the field of natural language processing. The keyphrase extraction algorithms(PhraseVecRank) is proposed based on phrase embedding. Firstly

a phrase vector construction model based on LSTM（Long Short-Term Memory） and CNN（Convolutional Neural Network） is designed to solve the semantic representation of complex phrases. Then

PhraseVecRank uses phrase embedding to calculate theme weight for each candidate phrase

and uses semantic similarity between candidate phrase embedding and co-occurrence information to calculate edge weight together

which can improve the extraction effect of keyphrases through topic weighted ranking. The experimental results verify that PhraseVecRank can effectively extract keyphrases covering the topic information of text

and the phrase embedding models we proposed can better represent the semantic information of phrases.

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references

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