基于语义和引用加权的文献主题提取研究

doi:10.13266/j.issn.0252-3116.2016.09.018

图书情报工作 ›› 2016, Vol. 60 ›› Issue (9): 131-138,146.DOI: 10.13266/j.issn.0252-3116.2016.09.018

基于语义和引用加权的文献主题提取研究

杨春艳¹, 潘有能², 赵莉¹

1. 宁波大学图书馆与信息中心宁波 315211;
2. 浙江大学公共管理学院杭州 310028

收稿日期:2016-01-04 修回日期:2016-04-20 出版日期:2016-05-05 发布日期:2016-05-05
作者简介:杨春艳(ORCID:0000-0002-1178-3851),助理馆员,E-mail:yangchunyan@nbu.edu.cn;潘有能,副教授,硕士生导师;赵莉,馆员。
基金资助:
本研究系国家社会科学基金项目"学术型大数据知识组织与服务标准研究"(项目编号:15FTQ002)研究成果之一。

Study on Topic Extraction of Literatures Based on Weighted Semantic and Citation Relation

Yang Chunyan¹, Pan Youneng², Zhao Li¹

1. Library and Information Center, Ningbo University, Ningbo 315211;
2. College of Public Administration, Zhejiang University, Hangzhou 310028

Received:2016-01-04 Revised:2016-04-20 Online:2016-05-05 Published:2016-05-05

摘要/Abstract

摘要：

[目的/意义]传统的文献主题提取方法主要是通过关键词、摘要、全文等提取文献的主题内容,使得主题内容不全面或存在"噪音",而从文献内容语义出发,结合引用内容提取文献的主题,能够更加准确地提取出多文档的主题内容。[方法/过程]提出一种面向多文档的基于语义和引用加权的科技文献主题提取算法,利用文献的引用内容和关键词构建Labeled-LDA主题模型,形成文档-主题概率向量,再根据K-means聚类方法聚类文档,提取每类文档集的主题内容。[结果/结论]以PubMed生物医学数据库中的数据作为实验数据,测试该方法的可靠性,结果证明该方法能够准确、全面地提取出多文档的主题内容。

关键词: Labeled-LDA模型, 引用内容, 主题提取

Abstract:

[Purpose/significance] The traditional methods of topic extraction mainly extract the themes of literatures by keywords, abstracts and full texts, but their results are not comprehensive or have noises. The method which starts from the semantic of literature content and is combined with the citation content, can extract the themes of literatures more accurately. [Result/conclusion] This article proposes a literature topic extracting algorithm based on weighted semantic and citation relation for multi-documents. It builds the Labeled-LDA topic Model with the citation content and keywords of literatures, gets documents-topics probability distribution. Then it clusters documents through the K-means clustering method and extracts the topics of each type of documents. [Result/conclusion] In the experimental part, the test data are downloaded from the PubMed database. The result shows that the method can accurately extract the theme of literatures.

Key words: Labeled-LDA Model, citation content, topic extraction

中图分类号:

G250

杨春艳, 潘有能, 赵莉. 基于语义和引用加权的文献主题提取研究[J]. 图书情报工作, 2016, 60(9): 131-138,146.

Yang Chunyan, Pan Youneng, Zhao Li. Study on Topic Extraction of Literatures Based on Weighted Semantic and Citation Relation[J]. LIS, 2016, 60(9): 131-138,146.

参考文献

[1] LUHN H P.The automatic creation of literature abstract [J].IBM journal of research and development,1958,2(2):159-165.
[2] BAXENDALE P E. Machine-made index for technical literature-an experiment [J]. Journal of research and development,1958, 2(4):354-361.
[3] BOKAETF M H, SAMETI H, LIU Y. Unsupervised approach to extract summary keywords in meeting domain[C]//Proceedings of the 23rd European signal processing conference.Piscataway: IEEE Press, 2015:1406-1410.
[4] BARNES C I, COSTANTINI L, PERSCHKE S. Automatic indexing using the SLC-II system [J]. Information processing & management, 1978, 14(2): 107-119.
[5] AWAJAN A. Keyword extraction from Arabic documents using term equivalence classes[J]. Crains Chicago business, 2015, 14(2):1-18.
[6] El-BELTAGY S R, RAFEA A. KP-Miner: a keyphrase extraction system for English and Arabic documents [J]. Information systems, 2009,34(1): 132-144.
[7] CHEN Y H, LU J L, MENG F T. Finding keywords in blogs: efficient keyword extraction in blog mining via user behaviors[J]. Expert systems with applications an international journal, 2014, 41(2):663-670.
[8] BRANDOW R, MITZE K, RAU L F. Automatic condensation of electronic publications by sentence selection [J].Information processing & management,1995,31(5):675-685.
[9] 韩客松,王永成.中文全文标引的主题词标引和主题概念标引方法[J].情报学报,2001,20(2):212-216.
[10] 何新贵,彭甫阳.中文文本的关键词自动抽取和模糊分类[J].中文信息学报,1999,13(1):9-15.
[11] 陈翀,罗鹏程,汪十红. 利用引用信息的关键词提取[J].图书情报工作,2014,58(1):101-108.
[12] SUBRAMANIYASWAMY V, PANDIAN S. Effective tag recommendation system based on topic ontology using Wikipedia and WordNet [J]. International journal of intelligent systems,2012,27(12) : 1034-1048.
[13] 刘琼琼,左万利,王英.面向网页的主题概念挖掘[J].计算机科学,2015,42(5):62-66.
[14] 曾聪,张东站.基于同义词词林和《知网》的短语主题提取[J].厦门大学学报(自然科学版),2015,54(2):263-269.
[15] INOUE K, MCCRACKEN N. Automated keyword extraction of learning materials using semantic relations[EB/OL].[2015-10-08].https://www.ideals.illinois.edu/bitstream/handle/2142/15050/abstract.pdf?sequence=2.
[16] 裘江南,罗志成,叶鑫.语义相关度算法在主题抽取中的适用性研究[J].情报学报,2009(1):34-39.
[17] 陈叶旺,王华珍,李海波,等.基于百度百科与文本分类的网络文本语义主题抽取方法[J].小型微型计算机系统,2012,33(12):2605-2610.
[18] REN F. An unsupervised cascade learning scheme for 'cluster-theme keywords' structure extraction from scientific papers [J]. Journal of information science, 2013, 40(2):167-170.
[19] XIE F, WU X, HU X. Keyphrase extraction based on semantic relatedness[C]//Proceedings of the 9th IEEE international conference on cognitive informatics. Piscataway: IEEE Press,2010: 308-312.
[20] GONENC E, ILYAS C. Using lexical chains for keyword extraction [J]. Information processing and management, 2007, 43(6): 1705-1714.
[21] 张云涛,龚玲,王永成.基于综合方法的文本主题句的自动抽取[J].上海交通大学学报,2006,40(5):771-774.
[22] ZHANG W, FENG W, WANG J. Integrating semantic relatedness and words' intrinsic features for keyword extraction[C]//Proceedings of the twenty-third international joint conference on artificial intelligence. Menlo Park:AAAI Press, 2013: 2225-2231.
[23] 方俊,郭雷,王晓东.基于语义的关键词提取算法[J].计算机科学,2008,35(6):148-151.
[24] DEERWESTER S C, DUMAIS S T, LANDAUER T K, et al. Indexing by latent semantic analysis[J]. Journal of the American Society for Information Science, 1990, 41(6): 391-407.
[25] HOFMANN T. Probabilistic latent semantic indexing[C]//Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. New York:ACM Press,1999:50-57.
[26] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation [J]. The journal of machine learning research, 2003, 3(3):993-1022.
[27] JINHEE P, JAEKWANG K,JEE-HYONG L. Keyword extraction for blogs based on content richness[J]. Journal of information science, 2014, 40(1): 38-49.
[28] GUO X, XIANG Y, CHEN Q, et al. LDA-based online topic detection using tensor factorization[J]. Journal of information science, 2013, 39(4): 459-468.
[29] 李湘东,张娇,袁满,基于LDA 模型的科技期刊主题演化研究[J].情报杂志,2014,33(7):115-121.
[30] RAMAGE D, HALL D, NALLAPATI R, et al. Labeled LDA: a supervised topic model for credit attribution inmulti-labeled corpora[C]//Proceedings of the 2009 conference on empirical methods in natural language processing.Stroudsburg:Association for Computational Linguistics,2009:248-256.
[31] 赵蓉英,曾宪琴,陈必坤.全文本引文分析-引文分析的新发展[J].图书情报工作,2014,58(9):129-134.
[32] SALTON G,BUCKLEY C. Term-weighting approaches in automatic text retrieval[J]. Information processing & management,1988, 24(5):513-523.
[33] MACQUEEN J. Some methods for classification and analysis of multivariate observations[C]//Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.Berkeley: University of California Press,1967: 281-297.

基于语义和引用加权的文献主题提取研究

Study on Topic Extraction of Literatures Based on Weighted Semantic and Citation Relation

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