自动关键词抽取研究综述
作者:
基金项目:

国家自然科学基金(61272260,61273320)


Review of Research in Automatic Keyword Extraction
Author:
Fund Project:

National Natural Science Foundation of China (61272260, 61273320)

  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [106]
  • |
  • 相似文献 [20]
  • |
  • 引证文献
  • | |
  • 文章评论
    摘要:

    自动关键词抽取是从文本或文本集合中自动抽取主题性或重要性的词或短语,是文本检索、文本摘要等许多文本挖掘任务的基础性和必要性的工作.探讨了关键词和自动关键词抽取的内涵,从语言学、认知科学、复杂性科学、心理学和社会科学等多个方面研究了自动关键词抽取的理论基础.从宏观、中观和微观角度,回顾和分析了自动关键词抽取的发展、技术和方法.针对目前广泛应用的自动关键词抽取方法,包括统计法、基于主题的方法、基于网络图的方法等,总结了其关键技术和研究进展.对自动关键词抽取的评价方式进行了分析,对自动关键词抽取面临的挑战和研究趋势进行了预测.

    Abstract:

    Automatic keyword extraction is to extract topical and important words or phrases form document or document set. It is a basic and necessary work in text mining tasks such as text retrieval and text summarization. This paper discusses the connotation of keyword extraction and automatic keyword extraction. In the light of linguistics, cognitive science, complexity science, psychology and social science, this paper studies the theoretical basis of automatic keyword extraction. From macro, meso and micro perspectives, the development, techniques and methods of automatic keyword extraction are reviewed and analyzed. This paper summarizes the current key technologies and research progress of automatic keyword extraction methods, including statistical methods, topic based methods, and network based methods. The evaluation approach of automatic keyword extraction is analyzed, and the challenges and trends of automatic keyword extraction are also predicted.

    参考文献
    [1] Feather JSP. Int'l Encyclopedia of Information and Library Science. 2nd ed., London & New York:Routledge, 2004. 38-96.
    [2] de Saussure F, Wrote; Liu L, Trans. Course in General Linguistics. In:Liu L, ed. Beijing:the Social Science Press, 2009. 37-49(in Chinese).
    [3] Liu Y. Computational Linguistics. Beijing:Tsinghua University Press, 2014. 121-132(in Chinese).
    [4] Baetens J. Conversations on cognitive cultural studies:Literature, language, and aesthetics. Leonardo, 2015,48(1):93-94.[doi:10.1162/LEON_r_00944]
    [5] Wang C, Zhang Q, Gan JP. Study on efficient complex network model. In:Yang Y, Ma M, eds. Proc. of the 2nd Int'l Conf. on Green Communications and Networks. Berlin, Heidelberg:Springer-Verlag, 2012. 159-164.[doi:10.1007/978-3-642-35398-7_20]
    [6] Lai YY, Li C, Goldwasser D, Neville J. Better together:Combining language and social interactions into a shared representation. In:КовтунH Н, ed. Proc. of the Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-10). San Diego:Association for Computational Linguistics, 2016. 29-33.[doi:10.18653/v1/W16-1405]
    [7] Even S. Graph Algorithms. 2nd ed., London:Cambridge University Press, 2011. 100-112.
    [8] Aronson E, Wilson TD, Akert RM, Wrote; Hou YB, Zhu Y, Trans. Social Psychology. 8th ed., Beijing:Mechanical Industry Press, 2014. 97-103(in Chinese).
    [9] Luhn HP. A statistical approach to mechanized encoding and searching of literary information. IBM Journal of Research & Development, 1957,1(4):309-317.[doi:10.1147/rd.14.0309]
    [10] Edmundson HP, Oswald VA. Automatic indexing and abstracting of the contents of documents. Planing Reserarch Corp, Document PRC R-126, ASTLA AD No.231606. Los Angeles:Planning Research Corp, 1959. 1-142.
    [11] Lois LE. Experiments in automatic indexing and extracting. Information Storage and Retrieval, 1970,6(4):313-330.[doi:10.1016/0020-0271(70)90025-2]
    [12] Turney PD. Learning algorithms for keyphrase extraction. Information Retrieval Journal, 2000,2(4):303-336.[doi:10.1023/A:1009976227802]
    [13] Frank E, Paynter GW, Witten IH, Gutwin C, Nevill-Manning CG. Domain-Specific keyphrase extraction. In:Dean T, ed. Proc. of the 16th Int'l Joint Conf. on Artificial Intelligence, ACM CIKM Int'l Conf. on Information & Knowledge Management. 1999. 668-673.
    [14] Hulth A. Improved automatic keyword extraction given more linguistic knowledge. In:Collins M, ed. Proc. of the. Conf. on Empirical Methods in Natural Language Processing (EMNLP). Sapporo, 2003. 216-223.[doi:10.3115/1119355.1119383]
    [15] Song M, Song IY, Hu X. KPSpotter:A flexible information gain-based keyphrase extraction system. In:Chiang R, Laender AHF, Lim EP, eds. Proc. of the 5th ACM Int'l Workshop on Web Information and Data Management. New Orleans, 2003. 50-53.[doi:10.1145/956699.956710]
    [16] Hong B, Zhen D. An extended keyword extraction method. In:Yang DH, ed. Proc. of the Int'l Conf. on Applied Physics and Industrial Engineering. 2012. 1120-1127.[doi:10.1016/j.phpro.2012.02.167]
    [17] Zhang C. Automatic keyword extraction from documents using conditional random fields. Journal of Computational Information Systems, 2008,3(4):1169-1180.
    [18] Suzuki S, Takatsuka H. Extraction of keywords of novelties from patent claims. In:Proc. of the 26th Int'l Conf. on Computational Linguistics:Technical Papers. 2016. 1192-1200.
    [19] Li D, Li S, Li W, Wang W, Qu W. A semi-supervised key phrase extraction approach:Learning from title phrases through a document semantic network. In:Kleber H, ed. Proc. of the ACL 2010 Conf. on Short Papers. Stroudsburg:Association for Computational Linguistics, 2010. 296-300.
    [20] Li DC, Li SJ. Hypergraph-Based inductive learning for generating implicit key phrases. In:Simpson S, ed. Proc. of the Int'l Conf. on Companion on World Wide Web. New York:ACM Press, 2011. 77-78.[doi:10.1145/1963192.1963232]
    [21] Lynn HM, Choi C, Choi J, Shin J, Kim P. The method of semi-supervised automatic keyword extraction for Web documents using transition probability distribution generator. In:Kim J, ed. Proc. of the Int'l Conf. on Research in Adaptive and Convergent Systems. Odense:ACM Press, 2016. 1-6.[doi:10.1145/2987386.2987399]
    [22] Siddiqi S, Sharan A. Keyword and keyphrase extraction techniques:A literature review. Int'l Journal of Computer Applications, 2015,109(2):18-23.[doi:10.5120/19161-0607]
    [23] Salton G, Buckley C. Term-Weighting approaches in automatic text retrieval. Information Processing & Management, 1988,24(5):513-523.[doi:10.1016/0306-4573(88)90021-0]
    [24] Yang KY. Research on automatic extraction algorithm based on improved TFIDF keywords[MS. Thesis]. Xiangtan:Xiangtan University, 2015(in Chinese with English abstract).
    [25] Huang L, Wu YP, Zhu QF. Research and improvement of keyword automatic extraction method. Computer Science, 2014,41(6):204-207(in Chinese with English abstract).
    [26] Besils R, Moschitti A, Pazienza M. A text classifier based on linguistic processing. In:Teresa PM, ed. Proc. of the Int'l Joint Conf. on Artificial Intelligence. UCAI, 1999. 36-40.
    [27] How BC, Narayanan K. An empirical study of feature selection for text categorization based on term weightage. In:Zhong J, ed. Proc. of the Int'l Conf. on Web Intelligence. Los Alamitos:IEEE Computer Society, 2004. 599-602.[doi:10.1109/WI.2004.10060]
    [28] Suzuki Y, Mitsukawa M, Kawagoe K. A image retrieval method using TFIDF based weighting scheme. In:Proc. of the Int'l Workshop on Database & Expert System Application. IEEE, 2008. 112-116.[doi:10.1109/DEXA.2008.106]
    [29] Liu L, Peng T. Clustering-Based method for positive and unlabeled text categorization enhanced by improved TFIDF. Journal of Information Science & Engineering, 2014,30(5):1463-1481.
    [30] Qin P, Xu W, Guo J. A novel negative sampling based on TFIDF for learning word representation. Neurocomputing, 2016,177:257-265.[doi:10.1016/j.neucom.2015.11.028]
    [31] Hofmann T. Probabilistic latent semantic indexing. In:Gey F, Hearst M, Tong R, eds. Proc. of the 22nd Annual Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. New York:ACM Press, 1999. 50-57.[doi:10.1145/312624.312649]
    [32] Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. Journal of Machine Learning Research, 2003,3:993-1022.
    [33] Liu Z, Huang W, Zheng Y, Sun M. Automatic keyphrase extraction via topic decomposition. In:Fosler-Lussier E, ed. Proc. of the Conf. on Empirical Methods in Natural Language Processing. Mit Stata Center, 2010. 366-376.
    [34] Ding Z, Qiu X, Zhang Q, Huang X. Learning topical translation model for microblog hashtag suggestion. In:Rossi F, ed. Proc. of the 23rd Int'l Joint Conf. on Artificial Intelligence. 2013. 2078-2084.
    [35] Pu X, Jin R, Wu G, Han D, Xue GR. Topic modeling in semantic space with keywords. In:Koh YS. Proc. of the 24th ACM Int'l Conf. on Information and Knowledge Management. New York:ACM Press, 2015. 1141-1150.[doi:10.1145/2806416.2806584]
    [36] Siu MH, Gish H, Chan A, Belfield W, Lowe S. Unsupervised training of an HMM-based self-organizing unit recognizer with applications to topic classification and keyword discovery. Computer Speech & Language, 2014,28(1):210-223.[doi:10.1016/j.csl.2013.05.002]
    [37] Song Y, Pan S, Liu S, Zhou MX, Qian W. Topic and keyword re-ranking for LDA-based topic modeling. In:Cheung D, Song IY, Chu W, Hu XH, Lin J, eds. Proc. of the Conf. on Information and Knowledge Management (CIKM 2009). Hong Kong:ACM Press, 2009. 1757-1760.[doi:10.1145/1645953.1646223]
    [38] Wei HX, Gao GL, Su XD. LDA-Based word image representation for keyword spotting on historical mongolian documents. In:Hirose A, Ozawa S, Doya K, Ikeda K, Lee M, Liu D, eds. Proc. of the Neural Information Processing (ICONIP). LNCS, 2016. 432-441.[doi:10.1007/978-3-319-46681-1_52]
    [39] Watts D, Strogatz S. Collective dynamics of ‘small world’ network. Nature, 1998,393(6684):440-442.[doi:10.1038/30918]
    [40] Barabási AL, Albert R. Emergence of scaling in random networks. Science, 1999,286(5439):509-512.[doi:10.1126/science.286. 5439.509]
    [41] Cancho RFI, Sole RV. The small world of human language. The Royal Society of London Series B-Biological Sciences, 2001, 268(1482):2261-2265.[doi:10.1098/rspb.2001.1800]
    [42] Quillian MR. Semantic networks. Approaches to Knowledge Representation Research Studies, 1968,23(92):1-50.
    [43] Ohara K, Saito K, Kimura M, Motoda H. Resampling-Based gap analysis for detecting nodes with high centrality on large social network. In:Cao T, Lim EP, Zhou ZH, Ho TB, Cheung D, Motoda H, eds. Proc. of the Advances in Knowledge Discovery and Data Mining (PAKDD). LNCS, 2015. 135-147.[doi:10.1007/978-3-319-18038-0_11]
    [44] Santos EE, Korah J, Murugappan V, Subramanian S. Effectively handling new relationship formations in closeness centrality analysis of social networks using anytime anywhere methodology. In:Cai ZP, ed. Proc. of the IEEE Int'l Conf. on Big Data and Cloud Computing. IEEE Computer Society, 2016. 354-361.[doi:10.1109/BDCloud-SocialCom-SustainCom.2016.60]
    [45] Ma HY, Lu P, Zhan ZQ, Huang XX, Wang RB. Research on complex network characteristics of micro-blog language. Computer Engineering and Applications, 2015,51(19):119-124(in Chinese with English abstract).
    [46] Lahiri S, Choudhury SR, Caragea C. Keyword and keyphrase extraction using centrality measures on collocation networks. Computer Science, 2014,26(1):1-16.
    [47] Boudin F. A comparison of centrality measures for graph-based keyphrase extraction. In:Convention N, Bureauthe V, eds. Proc. of the 6th Int'l Joint Conf. on Natural Language Processing (IJCNLP). 2013. 834-838.
    [48] Schluter N. Centrality measures for non-contextual graph-based unsupervised single document keyword extraction. Proc. of the Traitement Automatique des Langues Naturelles, 2014, 92(2):455-460.
    [49] Rousseau F, Vazirgiannis M. Main core retention on graph-of-words for single-document keyword extraction. In:Hanbury A, Kazai G, Rauber A, Fuhr N, eds. Proc. of the Advances in Information Retrieval (ECIR). LNCS. Springer Int'l Publishing, 2015. 382-393.[doi:10.1007/978-3-319-16354-3_42]
    [50] Tixier AJP, Malliaros FD, Vazirgiannis M. A graph degeneracy-based approach to keyword extraction. In:Patwardhan S, Pighinthe D, eds. Proc. of the 2016 Conf. on Empirical Methods in Natural Language Processing (EMNLP). Austin:Association for Computational Linguistics, 2016. 1860-1870.[doi:10.18653/v1/D16-1191]
    [51] Li JF, Lu XQ, Zhou SJ. Patent keyword indexing based on weighted complex graph model. New Technology of Library and Information Service, 2015,31(3):26-32(in Chinese with English abstract).
    [52] Ma L, Jiao LC, Bai L, Zhou YF, Dong LB. Research on a compound keywords detection method based on small world model. Journal of Chinese Information Processing, 2009,23(3):121-128(in Chinese with English abstract).[doi:10.3969/j.issn.1003-0077. 2009.03.016]
    [53] Zuo XF, Liu HL, Fan YJ, Zhao H. Research of text clustering algorithm based on conceptual semantic field. Journal of Intelligence, 2012,31(5):184-188+195(in Chinese with English abstract).
    [54] Li P. Study on center nodes of co-occurrence networks of six different languages[MS. Thesis]. Ji'nan:Shandong University, 2014(in Chinese with English abstract).
    [55] https://en.wikipedia.org/wiki/PageRank2017.
    [56] Mihalcea R, Tarau P. TextRank:Bringing order into text. In:Proc. of the EMNLP 2004. Unt Scholarly Works, 2004. 404-411.
    [57] Erkan G, Radev DR. LexRank:Graph-based lexical centrality as salience in text summarization. Artificial Intelligence Res. (JAIR), 2004,22(1):457-479.
    [58] Habibi M, Popescu-Belis A. Diverse keyword extraction from conversations. In:Vinogradova OI, ed. Proc. of the 51st Annual Meeting of the Association for Computational Linguistics. Sofia:Newdesign, 2013. 651-657.
    [59] Huang CC, Eskenazi M, Carbonell J, Ku LW, Yang PC. Cross-Lingual information to the rescue in keyword extraction. In:Koller A, Yusuke M, eds. Proc. of the 52nd Annual Meeting of the Association for Computational Linguistics:System Demonstrations. Baltimore:Association for Computational Linguistics, 2014. 1-6.
    [60] Huang CC, Chen MH, Yang PC. Bilingual keyword extraction and its educational application. In:Zervanou K, van Erp M, Alex B, eds. Proc. of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications. Beijing:Association for Computational Linguistics and Asian Federation of Natural Language Processing, 2015. 43-48.[doi:10.18653/v1/W15-4407]
    [61] Yang F, Zhu YS, Ma YJ. WS-Rank:Bringing sentences into graph for keyword extraction. In:Li F, et al., eds. Proc. of the APWeb. Switzerland, 2016. 474-477.[doi:10.1007/978-3-319-45817-5_49]
    [62] Rose SJ, Engel D, Cramer N, Cowley W. Automatic keyword extraction from individual documents. In:Berry MW, Kogan J, eds. Proc. of the Text Mining:Applications and Theory. 2010. 1-20.[doi:10.1002/9780470689646.ch1]
    [63] Liang WM, Huang CN, Li M, Lu BL. Extracting keyphrases from Chinese news articles using TextRank and query log knowledge. In:T'sou B, ed. Proc. of the 23rd Pacific Asia Conf. on Language, Information and Computation. 2009. 733-740.
    [64] Xia T. Study on keyword extraction using word position weighted TextRanl. New Technology of Library and Information Service, 2013,29(9):30-34(in Chinese with English abstract).
    [65] Liu T. Algorithm research of text keyword extraction based on complex network. Computer Application Research, 2016,33(2):365-370(in Chinese with English abstract).[doi:10.3969/j.issn.1001-3695.2016.02.010]
    [66] Zhang LJ, Li YL, Zeng QT, Lei JL, Yang P. Keyword extraction algorithm based on improved text rank. Journal of Beijing Institute of Graphic Communication, 2016,24(4):51-55(in Chinese with English abstract).[doi:10.3969/j.issn.1004-8626.2016. 04013]
    [67] Zhou ZH. Maching Learning. Beijing:Tsinghua University Press, 2016. 123-145(in Chinese).
    [68] Bandaru S, Ng AHC, Deb K. Data mining methods for knowledge discovery in multi-objective optimization:Part A-Survey. Expert Systems with Applications, 2017,70:139-159.[doi:10.1016/j.eswa.2016.10.015]
    [69] Onan A, Korukoglu S, Bulut H. Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 2016,57:232-247.[doi:10.1016/j.eswa.2016.03.045]
    [70] Yang K, Chen ZH, Cai Y. Improved automatic keyword extraction given more semantic knowledge. In:Du XY, ed. Proc. of the 21th Int'l Conf. on Database Systems for Advanced Applications. Springer-Verlag, 2016. 112-125.[doi:10.1007/978-3-319-32055-7_10]
    [71] Li GY, Wang HF. Improved automatic keyword extraction based on TextRank using domain knowledge. In:Zong C, ed. Proc. of the NLPCC. New York:Springer-Verlag, 2014. 403-413.[doi:10.1007/978-3-662-45924-9_36]
    [72] Xu SH, Kong F. Toward better keywords extraction. In:Zhou MQ, ed. Proc. of the Int'l Conf. on Asian Language Processing. IEEE, 2015. 181-184.[doi:10.1109/IALP.2015.7451561]
    [73] Bougouin A, Boudin F, Daille B. TopicRank:Graph-Based topic ranking for keyphrase extraction. In:Jiang J, Ku LW, eds. Proc. of the Int'l Joint Conf. on Natural Language Processing. ACL, 2013. 543-551.
    [74] Habibi M, Popescu-Belis A. Diverse keyword extraction from conversations. In:Navigli R, Chang JS, eds. Proc. of the 51st Annual Meeting of the Association for Computational Linguistics. Red Hook:Curran Associates, Inc., 2013. 651-657.
    [75] Marujo L, Ling W, Trancoso I, Dyer C, Black AW, Gershman A, de Matos DM, Neto JP, Carbonell J. Automatic keyword extraction on Twitter. In:Che WX, Zhou GD, eds. Proc. of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Int'l Joint Conf. on Natural Language Processing (Short Papers). Sweden:Taberg Media Group AB, 2015. 637-643.[doi:10.3115/v1/P15-2105]
    [76] Liu K, Xu LH, Zhao J. Extracting opinion targets and opinion words from online reviews. In:Kristina T, Hua W, eds. Proc. of the 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore:Association for Computational Linguistics, 2014. 314-324.
    [77] Xu H, Martin E, Mahidadia A. Extractive summarization based on keyword profile and language model. In:Mohammad SM, ed. Proc. of the NAACL-HLT 2015. Denver:Association for Computational Linguistics, 2015. 123-132.[doi:10.3115/v1/N15-1013]
    [78] Chahine CA, Chaignaud N, Kotowicz JP, Pécuchet JP. Context and keyword extraction in plain text using a graph representation. In:Proc. of the 4th Int'l Conf. on Signal Image Technology and Internet Bases Systems Bali (SITIS. INDONESIA.). Los Alamitos:IEEE Computer Society, 2008. 692-696.[doi:10.1109/SITIS.2008.47]
    [79] Wang X, Wang L, Li JW, Li SJ. Exploring simultaneous keyword and key sentence extraction:Improve graph-based ranking using Wikipedia. In:He Q, Melville P, Yin YL. Proc. of the ACM Int'l Conf. on Information & Knowledge Management. Maui:CIKM, 2012. 2619-2622.[doi:10.1145/2396761.2398706]
    [80] Zhou Z, Zou X, Lv X, Hu J. Research on weighted complex network based keywords extraction. In:Liu P, Su Q, eds. Proc. of the CLSW. Springer, Berlin, Heidelberg:Chinese Lexical Semantics, 2013. 442-452.[doi:10.1007/978-3-642-45185-0_47]
    [81] Liu XJ. Xie F, Wu XD. Graph based keyphrase extraction using LDA topic model. Journal of the China Society for Scientific and Technical Information, 2016,35(6):664-672(in Chinese with English abstract).[doi:10.3772/j.issn.1000-0135.2016.006.010]
    [82] Zhang JE. A Chinese keywords extraction approach based on TFIDF and word correlation. Journal of the China Society for Scientific and Technical Information, 2012,10:110-112,123(in Chinese with English abstract).[doi:10.13833/j.cnki.is.2012.10. 010]
    [83] Zhai ZW, Liu G, Liu YQ. Keywords mining method based on graph model. Software, 2012,33(8):9-13(in Chinese with English abstract).[doi:10.3969/j.issn.1003-6970.2012.08.002]
    [84] Chen YQ, Zhou RQ, Zhu HW, Li MT, Yin J. Mining patent knowledge for automatic keyword extraction. Journal of Computer Research and Development, 2016,53(8):1740-1752(in Chinese with English abstract).[doi:10.7544/issn1000-1239.2016. 20160195]
    [85] Saga R, Kobayashi H, Miyamoto T, Tsuji H. Measurement evaluation of keyword extraction based on topic coverage. In:Stephanidis C, ed. Proc. of the HCⅡ. Swizerland:Springer Int'l Publishing, 2014. 224-227.[doi:10.7544/issn1000-1239.2016. 20160195]
    [86] Liu XP, Wan CX, Liu DX, Liao GQ. Survey on spatial keyword search. Ruan Jian Xue Bao/Journal of Software, 2016,27(2):329-347(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4934.htm[doi:10.13328/j.cnki.jos.004934]
    附中文参考文献:
    [2] de Saussure F,著;刘丽,译.普通语言学教程.北京:商务印书馆,2009.37-49.
    [3] 刘颖.计算语言学(修订版).北京:清华大学出版社,2009.37-49.
    [8] Aronson E, Wilson TD, Akert RM,著;侯玉波,朱颖,译.社会心理学:阿伦森眼中的社会性动物.第8版,北京:机械工业出版社,2014. 97-103.
    [24] 杨凯艳.基于改进的TFIDF关键词自动提取算法研究[硕士学位论文].湘潭:湘潭大学,2015.
    [25] 黄磊,伍雁鹏,朱群峰.关键词自动提取方法的研究与改进.计算机科学,2014,41(6):204-207.
    [45] 马宏炜,陆蓓,谌志群,黄孝喜,王荣波.微博语言的复杂网络特征研究.计算机工程与应用,2015,51(19):119-124.
    [51] 李军锋,吕学强,周绍钧.带权复杂图模型的专利关键词标引研究.现代图书情报技术,2015,31(3):26-32.
    [52] 马力,焦李成,白琳,周雅夫,董洛兵.基于小世界模型的复合关键词提取方法研究.中文信息学报,2009,23(3):121-128.[doi:10. 3969/j.issn.1003-0077.2009.03.016]
    [53] 左晓飞,刘怀亮,范云杰,赵辉.基于概念语义场的文本聚类算法研究.情报杂志,2012,31(5):184-188+195.
    [54] 李萍.6种语言词同现网络中心节点研究[硕士学位论文].济南:山东大学,2014.
    [64] 夏天.词语位置加权TextRank的关键词抽取研究.现代图书情报技术,2013,29(9):30-34.
    [65] 刘通.基于复杂网络的文本关键词提取算法研究.计算机应用研究,2016,33(2):365-370.[doi:10.3969/j.issn.1001-3695.2016.02. 010]
    [66] 张莉婧,李业丽,曾庆涛,雷嘉丽,杨鹏.基于改进TextRank的关键词抽取算法.北京印刷学院学报,2016,24(4):51-55.[doi:10.3969/j.issn.1004-8626.2016.04.013]
    [67] 周志华.机器学习.北京:清华大学出版社,2016.
    [81] 刘啸剑,谢飞,吴信东.基于图和LDA主题模型的关键词抽取算法.情报学报,2016,35(6):664-672.[doi:10.3772/j.issn.1000-0135. 2016.006.010]
    [82] 张建娥.基于TFIDF和词语关联度的中文关键词提取方法.情报科学,2012,10:110-112,123.[doi:10.13833/j.cnki.is.2012.10.010]
    [83] 翟周伟,刘刚,吕玉琴.基于图模型的关键词挖掘算法.软件,2012,33(8):9-13.[doi:10.3969/j.issn.1003-6970.2012.08.002]
    [84] 陈忆群,周如旗,朱蔚恒.挖掘专利知识实现关键词自动抽取.计算机研究与发展,2016,53(8):1740-1752.[doi:10.7544/issn1000-1239.2016.20160195]
    [86] 刘喜平,万常选,刘德喜.空间关键词搜索研究综述.软件学报,2016,27(2):329-347. http://www.jos.org.cn/1000-9825/4934.htm[doi:10.13328/j.cnki.jos.004934]
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

赵京胜,朱巧明,周国栋,张丽.自动关键词抽取研究综述.软件学报,2017,28(9):2431-2449

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2017-01-26
  • 最后修改日期:2017-04-13
  • 在线发布日期: 2017-09-02
文章二维码
您是第19876377位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京市海淀区中关村南四街4号,邮政编码:100190
电话:010-62562563 传真:010-62562533 Email:jos@iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号