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中南大学学报(自然科学版)

Journal of Central South University

第46卷    第6期    总第250期    2015年6月

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文章编号:1672-7207(2015)06-2142-07
一种基于LDA模型的关键词抽取方法
朱泽德1, 2,李淼2,张健2,曾伟辉2,曾新华2

(1. 中国科学技术大学 自动化系,安徽 合肥,230026
2. 中国科学院 合肥智能机械研究所,安徽 合肥,230031
)

摘 要: 为解决现有方法未能综合考察文档主题的全面性、关键词的可读性以及差异性,提出一种基于文档隐含主题的关键词抽取新算法TFITF。算法根据大规模语料产生隐含主题模型计算词汇对主题的TFITF权重并进一步产生词汇对文档的权重,利用共现信息排序和选择相邻词汇形成候选关键短语,再使用相似性排除隐含主题一致的冗余短语。此外,从文档统计信息、词汇链和主题分析3方面来进行关键词抽取的对比测试,实验在1 040篇中文摘要及5 408个关键词构成的测试集上展开。结果表明,算法有效地提高文档关键词抽取的准确率与召回率。

 

关键字: 信息抽取;关键词抽取;LDA模型;主题相似性

A LDA-based approach to keyphrase extraction
ZHU Zede1, 2, LI Miao2, ZHANG Jian2, ZENG Weihui2, ZENG Xinhua2

1. Department of Automation, University of Science and Technology of China, Hefei 230026, China;
2. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China

Abstract:Due to the shortage of the comprehensive analysis of the coverage of document topics, the readability and difference of keyphrases, a new algorithm of keyphrase extraction TFITF based on the implicit topic model was put forward. The algorithm adopted the large-scale corpus and producted latent topic model to calculate the TFITF weight of vocabulary on the topic and further generate the weight of vocabulary on the document. And adjacent lexical was ranked and picked out as candidate keyphrases based on co-occurrence information. Then according to the similarity of vocabulary topics, redundant phrases were eliminated. In addition, the comparative experiments of candidate keyphrases were executed by document statistical information, vocabulary chain and topic information. The experimental results, which were carried out on an evaluation dataset including 1 040 Chinese documents and 5 408 standard keyphrases, demonstrate that the method can effectively improve the precision and recall of keyphrase extraction.

 

Key words: information extraction; keyphrase extraction; LDA model; topic similarity

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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