Abstract
Traditional supervised keyphrase extraction models depend on the features of labeled keyphrases while prevailing unsupervised models mainly rely on global structure of the word graph, with nodes representing candidate words and edges/links capturing the co-occurrence between words. However, the local context information of the word graph can not be exploited in existing unsupervised graph-based keyphrase extraction methods and integrating different types of information into a unified model is relatively unexplored. In this paper, we propose a new word embedding model specially for keyphrase extraction task, which can capture local context information and incorporate them with other types of crucial information into the low-dimensional word vector to help better extract keyphrases. Experimental results show that our method consistently outperforms 7 state-of-the-art unsupervised methods on three real datasets in Computer Science area for keyphrase extraction.
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References
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Sebastopol (2009)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Florescu, C., Caragea, C.: Positionrank: an unsupervised approach to keyphrase extraction from scholarly documents. In: Proceedings of ACL, pp. 1105–1115 (2017)
Gollapalli, S.D., Caragea, C.: Extracting keyphrases from research papers using citation networks. In: Proceedings of AAAI, pp. 1629–1635 (2014)
Gollapalli, S.D., Li, X., Yang, P.: Incorporating expert knowledge into keyphrase extraction. In: Proceedings of AAAI, pp. 3180–3187 (2017)
Lee, D.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Liu, Y., Liu, Z., Chua, T.S., Sun, M.: Topical word embeddings. In: Proceedings of AAAI, pp. 2418–2424 (2015)
Liu, Z., Huang, W., Zheng, Y., Sun, M.: Automatic keyphrase extraction via topic decomposition. In: Proceedings of EMNLP, pp. 366–376 (2010)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., Chi, Y.: Deep keyphrase generation. In: Proceedings of ACL, pp. 582–592 (2017)
Mihalcea, R., Tarau, P.: Textrank: bringing order into text. In: Proceedings of EMNLP, pp. 404–411 (2004)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp. 3111–3119. MIT Press (2013)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab (1999)
Piotr, B., Edouard, G., Armand, J., Tomas, M.: Enriching word vectors with subword information. TACL 5, 135–146 (2017)
Porter, M.F.: An algorithm for suffix stripping. Program Electron. Libr. Inf. Syst. 40(3), 211–218 (2006)
Shi, B., Lam, W., Jameel, S.: Jointly learning word embeddings and latent topics. In: Proceedings of SIGIR, pp. 375–384 (2017)
Sterckx, L., Demeester, T., Deleu, J.: Topical word importance for fast keyphrase extraction. In: Proceedings of WWW, pp. 121–122 (2015)
Tang, J., Qu, M., Mei, Q.: Pte: predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of SIGKDD, pp. 1165–1174 (2015)
Tang, Y., Huang, W., Liu, Q., Zhang, B.: Qalink: enriching text documents with relevant Q&A site contents. In: Proceedings of CIKM, pp. 3159–3168 (2017)
Teneva, N., Cheng, W.: Salience rank: efficient keyphrase extraction with topic modeling. In: Proceedings of ACL, pp. 530–535 (2017)
Wan, X., Xiao, J.: Single document keyphrase extraction using neighborhood knowledge. In: Proceedings of AAAI, pp. 855–860 (2008)
Wang, F., Wang, Z., Wang, S., Li, Z.: Exploiting description knowledge for keyphrase extraction. In: Pham, D.-N., Park, S.-B. (eds.) PRICAI 2014. LNCS (LNAI), vol. 8862, pp. 130–142. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13560-1_11
Wang, R., Liu, W., McDonald, C.: Corpus-independent generic keyphrase extraction using word embedding vectors. In: Proceedings of DL-WSDM, pp. 39–46 (2015)
Zhang, W., Feng, W., Wang, J.: Integrating semantic relatedness and words’ intrinsic features for keyword extraction. In: Proceedings of IJCAI, pp. 2225–2231 (2013)
Zhang, Y., Chang, Y., Liu, X., Gollapalli, S.D., Li, X., Xiao, C.: Mike: keyphrase extraction by integrating multidimensional information. In: Proceedings of CIKM, pp. 1349–1358 (2017)
Zhang, Z., Gao, J., Ciravegna, F.: Semre-rank: improving automatic term extraction by incorporating semantic relatedness with personalised pagerank. ACM Trans. Knowl. Discov. Data (TKDD) 12(5), 57:1–57:41 (2018)
Acknowledgements
This work was partially supported by grants from the National Natural Science Foundation of China (Nos. U1933114, 61573231) and Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province (No. CICIP2018004).
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Zhang, Y., Liu, H., Shi, B., Li, X., Wang, S. (2020). WEKE: Learning Word Embeddings for Keyphrase Extraction. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_19
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DOI: https://doi.org/10.1007/978-3-030-60290-1_19
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