Abstract
We report research on semantic relations extraction to build taxonomies. The state of the art approaches are based on text corpus or on domain texts acquisition to accurately characterize the domain of interest. We analyzed the application of unsupervised methods for ontology building using a collection of opinion reviews in Spanish and the Web. We present some results and discuss the obtained relations.
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References
Aguado de Cea, G., Álvarez de Mon, I., Montiel-Ponsoda, E.: From linguistic patterns to ontology structures. In: Proceedings of the 8th International Conference on Terminology and Artificial Intelligence (2009), http://ceur-ws.org/Vol-578/paper15.pdf
Barrón-Cedeño, G., Sierra, P., Drouin, S.: Ananiadou. An Improved Automatic Term Recognition Method for Spanish. Proceeding of Computational Linguistics and Intelligent Text Processing, 125–136 (2009)
Cambria, E., Poria, S., Gelbukh, A., Kwok, K.: Sentic API: A Common-Sense Based API for Concept-Level Sentiment Analysis. In: Proceedings of the 4th Workshop on Making Sense of Microposts (#Microposts2014), co-located with the 23rd International World Wide Web Conference (WWW 2014), Seoul, Korea. CEUR Workshop Proceedings, vol. 1141, pp. 19–24, CEUR-WS.org (April 7, 2014)
Cimiano, P., Pivk, A., Schmidt-Thieme, L., Staab, S.: Learning Taxonomic Relations from Heterogeneous Sources of Evidence. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Evaluation and Applications. Frontiers in Artificial Intelligence, vol. 123, pp. 59–73. IOS Press, Amsterdam (2005a)
Cimiano, P., Hotho, A., Staab, S.: Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis. J. Artif. Intell. Res. (JAIR) 24, 305–339 (2005b)
Frantzi, K., Ananiadou, S., Mima, H.: Automatic recognition of multi-word terms: the C-value/NC-value method. International Journal on Digital Libraries 3(2), 115–130 (2000)
Gaizauskas, R., Demetriou, G., Humphreys, K.: Term recognition and classification in biological science journal articles. In: Proceedings of Workshop on Computational Terminology for Medical and Biological Applications, pp. 37–44 (2000)
Gelbukh, A.F., Bolshakov, I.A.: Internet, a true friend of translator: the Google wildcard operator. International Journal of Translation 18(1-2), 41–48 (2006)
Gelbukh, A., Sidorov, G.: Approach to construction of automatic morphological analysis systems for inflective languages with little effort. In: Gelbukh, A. (ed.) CICLing 2003. LNCS, vol. 2588, pp. 215–220. Springer, Heidelberg (2003)
Giuliano, C., Lavelli, A., Pighin, D., Romano, L.: FBK-IRST: Kernel Methods for Semantic Relation Extraction. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pp. 141–144 (2007)
Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: 14th International Conference on Computational Linguistics, France, pp. 539–545 (1992)
Kilgarriff, A.: Googleology is bad science. Computational Linguistics 33, 147–151 (2007)
Kim, S.N., Baldwin, T., Kan, M.-y.: An Unsupervised Approach to Domain-Specific Term Extraction. In: Proceedings of the Australasian Language Technology Association Workshop (ALTW:B), pp. 94–98 (2009)
Liu, X., Song, Y., Liu, S., Wang, H.: Automatic Taxonomy Construction from Keywords. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1433–1441 (2012)
Ortega, R., Aguilar, C., Villaseñor, L., Montes, M., Sierra, G.: Hacia la identificación de relaciones de hiponimia/hiperonimia en Internet. Revista Signos. Estudios de Lingüística 44(75), 68–84 (2011)
Padró, L., Collado, M., Reese, S., Lloberes, M., Castellón, I.: FreeLing 2.1: Five Years of Open-Source Language Processing Tools. In: Proceedings of 7th Language Resources and Evaluation Conference (LREC 2010), ELRA La Valletta, Malta (2010)
Poria, S., Agarwal, B., Gelbukh, A., Hussain, A., Howard, N.: Dependency-Based Semantic Parsing for Concept-Level Text Analysis. In: Gelbukh, A. (ed.) CICLing 2014, Part I. LNCS, vol. 8403, pp. 113–127. Springer, Heidelberg (2014)
Poria, S., Cambria, E., Winterstein, G., Huang, G.-B.: Sentic patterns: Dependency-based rules for concept-level sentiment analysis. Knowledge-Based Systems 69, 45–63 (2014)
Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Presutti, V., Stankovic, M., Cambria, E., Cantador, I., Di Iorio, A., Di Noia, T., Lange, C., Reforgiato Recupero, D., Tordai, A. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 41–47. Springer, Heidelberg (2014)
Sánchez, D., Moreno, A.: A methodology for knowledge acquisition from the web. KES Journal 10(6), 453–475 (2006)
Shah, N., Chao, K.-M., Zlamaniec, T., Matei, A.: Ontology for Home Energy Management Domain. DICTAP (2), 337–347 (2011)
Xu, F., Kurz, D., Piskorski, J., Schmeier, S.: A Domain Adaptive Approach to Automatic Acquisition of Domain Relevant Terms and their Relations with Bootstrapping. In: Proceedings of the 3rd International Conference on Language Resources an Evaluation, LREC 2002 (2002)
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Galicia-Haro, S.N., Gelbukh, A. (2014). Extraction of Semantic Relations from Opinion Reviews in Spanish. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Human-Inspired Computing and Its Applications. MICAI 2014. Lecture Notes in Computer Science(), vol 8856. Springer, Cham. https://doi.org/10.1007/978-3-319-13647-9_18
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DOI: https://doi.org/10.1007/978-3-319-13647-9_18
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