基于邻居网络的科学文献关键词提取

doi:10.16451/j.cnki.issn1003-6059.201808007

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摘要

从单个文档中直接提取关键词不能满足关键词提取的精度要求,而现有基于邻居信息的关键词提取相关研究又耗时较长.因此,文中提出利用科学文献中共同作者关系以构建邻居网络,并联合使用这些邻居网络信息及文档本身内容提取关键词的方法.在此基础上,进一步提出利用领域知识中高频度共现词对以提取关键词,获得更高质量的关键词的方法.实验表明,文中方法性能较优.

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	黄晓玲
	王浩
	李磊
	伏明兰

关键词 ：共同作者网络, 邻居网络, 关键词提取, 自然语言处理, 文本处理

Abstract：

Extracting keywords directly from a single document cannot satisfy the precision requirements of keyphrase extraction, and the existing methods for keyphrase extraction based on neighbor information are time-consuming. In this paper, common author relations in research papers are utilized to build a neighbor network, and neighbor network information as well as document content is used to extract keyphrases. Based on those, high frequency pairs of phrase co-occurrence in domain are incorporated to further acquire high-quality keyphrases. Experimental results demonstrate that the proposed method is more computationally efficient than the existing methods.

Key words： Common-Author Network Neighborhood Network Keyphrase Extraction Natural Language Processing Text Processing

收稿日期: 2018-03-19

ZTFLH:

TP 391

基金资助:

国家重点研发计划项目(No.2016YFB1000901)、国家自然科学基金项目(No.61503114)、安徽省教育厅重点项目(No.KJ2017A418)资助

通讯作者: 黄晓玲,博士研究生,讲师,主要研究方向为自然语言处理、数据挖掘.E-mail:hxl@chzu.edu.cn.

作者简介: 王浩,博士,教授,主要研究方向为智能计算理论与软件、分布式智能系统、复杂系统建模.E-mail:jsjxwangh@hfut.edu.cn 李磊,博士,副教授,主要研究方向为数据挖掘、社交计算、图计算.E-mail:lilei@hfut.edu.cn. 伏明兰,博士研究生,主要研究方向为智能计算、人工智能.E-mail:fml685@163.com.

引用本文:

黄晓玲, 王浩, 李磊, 伏明兰. 基于邻居网络的科学文献关键词提取[J]. 模式识别与人工智能, 2018, 31(8): 750-762. HUANG Xiaoling, WANG Hao, LI Lei, FU Minglan. Keyphrase Extraction from Research Papers Using Neighborhood Networks. , 2018, 31(8): 750-762.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201808007 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I8/750

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