Abstract
Keyphrase extraction is the task of identifying a set of phrases that best represent a natural language document. It is a fundamental and challenging task that assists publishers to index and recommend relevant documents to readers. In this article, we introduce C-Rank, a novel unsupervised approach to automatically extract keyphrases from single documents by using concept linking. Our method explores Babelfy to identify candidate keyphrases, which are weighted based on heuristics and their centrality inside a co-occurrence graph where keyphrases appear as vertices. It improves the results obtained by graph-based techniques without training nor background data inserted by users. Evaluations are performed on SemEval and INSPEC datasets, producing competitive results with state-of-the-art tools. Furthermore, C-Rank generates intermediate structures with semantically annotated data that can be used to analyze larger textual compendiums, which might improve domain understatement and enrich textual representation methods.
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References
Augenstein, I., Das, M., Riedel, S., Vikraman, L., McCallum, A.: SemEval 2017 task 10: ScienceIE - extracting keyphrases and relations from scientific publications. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 546–555. Association for Computational Linguistics (2017)
Beliga, S., Meštrović, A., Martinčić-Ipšić, S.: An overview of graph-based keyword extraction methods and approaches. J. Inf. Organ. Sci. 39(1), 1–20 (2015)
Boudin, F.: A comparison of centrality measures for graph-based keyphrase extraction. In: International Joint Conference on NLP, pp. 834–838 (2013)
Bougouin, A., Boudin, F., Daille, B.: TopicRank: graph-based topic ranking for keyphrase extraction. In: International Joint Conference on Natural Language Processing (IJCNLP), pp. 543–551 (2013)
Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (volume 1: Long Papers), vol. 1, pp. 1262–1273 (2014)
Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 216–223. Association for Computational Linguistics (2003)
Kim, S.N., Medelyan, O., Kan, M.Y., Baldwin, T.: Automatic keyphrase extraction from scientific articles. Lang. Resour. Eval. 47(3), 723–742 (2013)
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404–411 (2004)
Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to WordNet: an on-line lexical database. Int. J. Lexicogr. 3(4), 235–244 (1990)
Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. Comput. Linguist. 2, 231–244 (2014)
Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)
Ortiz, R., Pinto, D., Tovar, M., Jiménez-Salazar, H.: BUAP: an unsupervised approach to automatic keyphrase extraction from scientific articles. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 174–177. Association for Computational Linguistics (2010)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report 1999–66, Stanford InfoLab, November 1999. previous number = SIDL-WP-1999-0120
Acknowledgements
This work was financially supported by the São Paulo Research Foundation (FAPESP) (grants #2017/02325-5 and #2013/08293-7) (The opinions expressed in here are not necessarily shared by the financial support agency.) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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Tosi, M.D.L., dos Reis, J.C. (2019). C-Rank: A Concept Linking Approach to Unsupervised Keyphrase Extraction. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds) Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, vol 1057. Springer, Cham. https://doi.org/10.1007/978-3-030-36599-8_21
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DOI: https://doi.org/10.1007/978-3-030-36599-8_21
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