Applying OWA Operator in the Semantic Processing for Automatic Keyphrase Extraction

  • Manuel Barreiro-Guerrero
  • Alfredo Simón-CuevasEmail author
  • Yamel Pérez-Guadarrama
  • Francisco P. Romero
  • José A. Olivas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)


The automatic keyphrases extraction from texts is a useful task for many computational systems in the natural language processing and text mining fields. Although several solutions to this problem have been developed, the semantic analysis has been one of the linguistic features less exploited in the most reported proposal, causing that the obtained results still show low accuracy and performance rates. This paper presents an unsupervised method for keyphrase extraction, which is based on the use of lexical-syntactic patterns for extracting information from texts and a fuzzy modelling of topics. An OWA operator which combines several semantics measures has been applied in the topic modelling process. This new approach was evaluated with Inspec and 500N-KPCrowd datasets and compared with other reported systems, obtaining promising results.


Automatic keyphrase extraction Linguistic patterns Topic modelling Semantic processing OWA operator 



This work has been partially supported by FEDER and the State Research Agency (AEI) of the Spanish Ministry of Economy and Competition under grant MERINET: TIN2016-76843-C4-2-R (AEI/FEDER, UE).


  1. 1.
    Bougouin, A., Boudin, F., Daille, B.: TopicRank: graph-based topic ranking for keyphrase extraction. In: Proceedings of the 6th International Joint Conference on NLP, pp. 543–551 (2013)Google Scholar
  2. 2.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)CrossRefGoogle Scholar
  3. 3.
    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 ACL, pp. 1262–1273 (2014)Google Scholar
  4. 4.
    Lehmann, J., et al.: DBpedia - a large-scale multilingual knowledge base extracted from Wikipedia. Semant. Web J. 1, 1–27 (2012)CrossRefGoogle Scholar
  5. 5.
    Li, Y., McLean, D., Bandar, Z.A., O’Shea, J.D., Crockett, K.: Sentence similarity based on semantic nets and corpus statistics. IEEE Trans. Knowl. Data Eng. 18, 1138–1150 (2006)CrossRefGoogle Scholar
  6. 6.
    Liu, Z., Li, P., Zheng, Y., Sun, M.: Clustering to find exemplar terms for keyphrase extraction. In: Proceedings of the 2009 Conference on Empirical Methods in NLP, pp. 257–266 (2009)Google Scholar
  7. 7.
    Liu, X., Han, S.: Orness and parameterized RIM quantifier aggregation with OWA operators: a summary. Int. J. Approx. Reason. 48(1), 77–97 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: Proceedings of the 2003 Conference on Empirical Methods in NLP, pp. 216–223 (2003)Google Scholar
  9. 9.
    Marujo, L., Ribeiro, R., de Matos, D.M., Neto, J.P., Gershman, A., Carbonell, J.: Key phrase extraction of lightly filtered broadcast news. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2012. LNCS (LNAI), vol. 7499, pp. 290–297. Springer, Heidelberg (2012). Scholar
  10. 10.
    Merrouni, Z.A., Frikh, B., Ouhbi, B.: Automatic keyphrase extraction: an overview of the state of the art. In: Proceedings of the 4th IEEE International Colloquium on Information Science and Technology, pp. 306–313 (2016)Google Scholar
  11. 11.
    Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Proceedings of the 2004 Conference on Empirical Methods in NLP, pp. 404–411 (2004)Google Scholar
  12. 12.
    Müllner, D.: Modern hierarchical, agglomerative clustering algorithms. CoRR, abs/1109.2378 (2011)Google Scholar
  13. 13.
    Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet::Similarity - measuring the relatedness of concepts. In: Proceedings of the 19th National Conference on Artificial Intelligence (AAAI 2004), pp. 1024–1025 (2004)Google Scholar
  14. 14.
    Pérez-Guadarrama, Y., Rodríguez, A., Simón-Cuevas, A., Hojas-Mazo, W., Olivas, J.A.: Combinando patrones léxico-sintácticos y análisis de tópicos para la extracción automática de frases relevantes en textos. Procesamiento del Lenguaje Natural 59, 39–46 (2017)Google Scholar
  15. 15.
    Rafiei-Asl, J., Nickabadi, A.: TSAKE: a topical and structural automatic keyphrase extractor. Appl. Soft Comput. J. 58, 620–630 (2017)CrossRefGoogle Scholar
  16. 16.
    Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic keyword extraction from individual documents. Text Mining: Applications and Theory, pp. 1–20 (2010)Google Scholar
  17. 17.
    Teneva, N., Cheng, W.: Salience rank: efficient keyphrase extraction with topic modeling. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 530–535 (2017)Google Scholar
  18. 18.
    Yager, R.R.: On ordered weighted averaging operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern. 18, 183–190 (1988)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Yager, R.: Quantifier guided aggregation using OWA operators. Int. J. Intell. Syst. 11, 49–73 (1996)CrossRefGoogle Scholar
  20. 20.
    Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages. Comput. Maths. Appl. 9, 149–184 (1983)MathSciNetCrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Universidad Tecnológica de La Habana José Antonio EcheverríaMarianaoCuba
  2. 2.Centro de Aplicaciones de Tecnologías de Avanzada (CENATAV)PlayaCuba
  3. 3.Universidad de Castilla-La ManchaCiudad RealSpain

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