Abstract
Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduces more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.
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References
Wikipedia. http://en.wikipedia.org/
Bavelas, A.: Communication patterns in task-oriented groups. J. Acoust. Soc. Am. 22(6), 725–730 (1950)
Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - a crystallization point for the web of data. J. Web Sem. 7(3), 154–165 (2009)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Boudin, F.: A comparison of centrality measures for graph-based keyphrase extraction. In: IJCNLP 2013, pp. 834–838 (2013)
Cilibrasi, R., Vitányi, P.M.B.: The Google similarity distance (2004). CoRR, abs/cs/0412098
Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)
Grineva, M.P., Grinev, M.N., Lizorkin, D.: Extracting key terms from noisy and multitheme documents. In: WWW 2009, pp. 661–670 (2009)
Hammouda, K.M., Matute, D.N., Kamel, M.S.: CorePhrase: keyphrase extraction for document clustering. In: Perner, P., Imiya, A. (eds.) MLDM 2005. LNCS, vol. 3587, pp. 265–274. Springer, Heidelberg (2005). doi:10.1007/11510888_26
Haveliwala, T.H.: Topic-sensitive PageRank. In: WWW 2002, pp. 517–526 (2002)
Huang, C., Tian, Y., Zhou, Z., Ling, C.X., Huang, T.: Keyphrase extraction using semantic networks structure analysis. In: ICDM 2006, pp. 275–284 (2006)
Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: EMNLP 2003, pp. 216–223 (2003)
Hulth, A.: Reducing false positives by expert combination in automatic keyword indexing. In: RANLP 2003, pp. 367–376 (2003)
Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 23–26 July 2002, pp. 538–543 (2002)
Jiang, X., Hu, Y., Li, H.: A ranking approach to keyphrase extraction. In: SIGIR 2009, pp. 756–757 (2009)
Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: ICML 2015, pp. 957–966 (2015)
Liu, Z., Huang, W., Zheng, Y., Sun, M.: Automatic keyphrase extraction via topic decomposition. In: EMNLP, pp. 366–376 (2010)
Liu, Z., Li, P., Zheng, Y., Sun, M.: Clustering to find exemplar terms for keyphrase extraction. In: EMNLP 2009, pp. 257–266 (2009)
Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–136 (1982)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: ACL 2014, pp. 55–60 (2014)
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: EMNLP 2004, pp. 404–411 (2004)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR (2013)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web (1999)
Russell, S.J., Norvig, P.: Artificial Intelligence - A Modern Approach: the Intelligent Agent Book. Prentice Hall Series in Artificial Intelligence. Prentice Hall, Englewood Cliffs (1995)
Tsatsaronis, G., Varlamis, I., Nørvåg, K.: SemanticRank: ranking keywords and sentences using semantic graphs. In: COLING 2010, pp. 1074–1082 (2010)
Turney, P.D.: Learning to extract keyphrases from text (2002). CoRR, cs.LG/0212013
Wan, X., Xiao, J.: Exploiting neighborhood knowledge for single document summarization and keyphrase extraction. ACM Trans. Inf. Syst. 28(2), 8 (2010)
Wan, X., Yang, J., Xiao, J.: Towards an iterative reinforcement approach for simultaneous document summarization and keyword extraction. In: ACL 2007, vol. 7, pp. 552–559 (2007)
Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: KEA: practical automatic keyphrase extraction. In: Proceedings of the Fourth ACM Conference on Digital Libraries, pp. 254–255 (1999)
Youn, E., Jeong, M.K.: Class dependent feature scaling method using naive Bayes classifier for text datamining. Pattern Recogn. Lett. 30(5), 477–485 (2009)
Acknowledgements
This work is supported by Research Grant Council of Hong Kong SAR No. 14221716 and The Chinese University of Hong Kong Direct Grant No. 4055048 and NSFC under grant Nos. 61622201, 61532010, 61370055, 61402020 and Ph.D. Programs Foundation of Ministry of Education of China No. 20130001120021.
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Shi, W., Zheng, W., Yu, J.X., Cheng, H., Zou, L. (2017). Keyphrase Extraction Using Knowledge Graphs. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_11
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