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数据分析与知识发现  2020, Vol. 4 Issue (7): 76-86     https://doi.org/10.11925/infotech.2096-3467.2020.0071
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
面向中文学术文本的单文档关键短语抽取 *
夏天()
中国人民大学数据工程与知识工程教育部重点实验室 北京 100872;中国人民大学信息资源管理学院 北京 100872
Extracting Key-phrases from Chinese Scholarly Papers
Xia Tian()
Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education,Renmin University of China, Beijing 100872, China;School of Information Resource Management, Renmin University of China, Beijing 100872, China
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摘要 

目的】自动抽取中文学术文本中的关键短语,为学术文本挖掘提供短语级别的概念表达。【方法】引入内部凝聚度和边界自由度两个指标,分别度量短语内部的紧密程度和短语边界的自由组配能力,实现中文双词短语的权威度计算,并与位置加权关键词抽取结果进行融合排序,在此基础上选取TopN个元素生成关键短语。【结果】在构建的中文学术论文数据集上,关键短语抽取算法PhraseRank在准确率、召回率和考虑排序位置的R-MAP评价指标方面,均大幅度优于传统的关键词抽取算法WordRank,其中,R-MAP值相对提升超过了128%。【局限】 未识别三个及以上词语构成的关键短语。【结论】相比于关键词,PhraseRank抽取得到的关键短语,与人工标记结果的一致性更高,更能体现中文学术文本的概念表达特点。

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夏天
关键词 关键短语抽取学术文本挖掘TextRank词图    
Abstract

[Objective] This paper propose a new method to extract key-phrases from Chinese scholarly articles, aiming to provide concept representation at phrase level for academic text mining.[Methods] First, we introduced the cohesion and freedom concepts to measure the internal tightness of phrases and free collocation ability of boundary words. It helped us compute the authority of bi-word phrases. Then, we merged our list with phrases extracted by position-weighted method. Finally, the TopN elements were retrieved as the final key phrases.[Results] We examined the proposed PhraseRank method with Chinese academic papers, and found its precision, recall and R-MAP values were significantly higher than those of the traditional WordRank algorithm. Among them, the R-MAP value increased by more than 128%.[Limitations] Our method could not identify key phrases with three or more words.[Conclusions] The keyphrases extracted by PhraseRank, which are more consistent with manually labeled results than keywords, effectively describe characteristics of Chinese scholarly papers.

Key wordsKey-phrase Extraction    Academic Text Mining    TextRank    Word Graph
收稿日期: 2020-02-01      出版日期: 2020-07-25
ZTFLH:  G353  
基金资助:*本文系国家社会科学基金重大项目“大数据环境下政务信息资源归档与管理研究”的研究成果之一(17ZDA293)
通讯作者: 夏天     E-mail: xiat@ruc.edu.cn
引用本文:   
夏天. 面向中文学术文本的单文档关键短语抽取 *[J]. 数据分析与知识发现, 2020, 4(7): 76-86.
Xia Tian. Extracting Key-phrases from Chinese Scholarly Papers. Data Analysis and Knowledge Discovery, 2020, 4(7): 76-86.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0071      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I7/76
Fig.1  关键短语抽取的实现流程
Fig.2  论文摘要文本构成的词图片断
Fig.3  双词短语识别示意图
构词数量 平均字符长度 出现次数 占比 累计占比
1 3.34 20 303 28.07% 28.07%
2 4.33 39 028 53.95% 82.02%
3 5.95 10 005 13.83% 95.85%
4 7.46 2 142 2.96% 98.81%
5 9.48 476 0.66% 99.47%
6 10.55 218 0.30% 99.77%
7 12.65 79 0.11% 99.88%
8 15.59 37 0.05% 99.93%
9 17.07 14 0.02% 99.95%
10 16.18 22 0.03% 99.98%
其他 - 13 0.02% 100.00%
Table 1  数据集中关键短语的统计信息
Fig.4  PharseRank与WordRank的PRF值对比
Fig.5  两种算法的MAP@N变化情况
算法 MAP@3 MAP@5 MAP@7 MAP@10 R-MAP
WordRank 0.070 0.083 0.087 0.091 0.077
PhraseRank 0.164 0.188 0.201 0.211 0.176
Table 2  WordRank、PhraseRank的MAP@NR-MAP对比
文档标题 人工标记 WordRank PhraseRank
面向安全教育的儿童阅读推广研究 图书馆, 儿童阅读推广, 安全 儿童, 推广, 阅读 儿童, 阅读推广, 教育
图书馆电子书馆配研究 馆配市场, 电子书馆配, 图书馆 电子书, 图书馆, 文献 图书馆电子书, 文献, 市场
国外基于情感角度的信息搜寻行为研究进展 情感, 认知, 信息搜寻行为 情感, 信息, 搜寻 情感因素, 影响信息, 搜寻
试析大数据在电子文件管理中的应用 大数据, 电子文件管理 文件, 电子 电子文件, 文件管理
虚实融合的图书馆空间互动服务模式研究 图书馆, 实体空间, 虚拟空间 图书馆, 空间, 服务 图书馆空间, 服务模式, 互动服务
Table 3  R-AP=0文档的抽取结果示例
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