Espinosa-Anke, LuisRonzano, FrancescoSaggion, Horacio2018-12-072018-12-072015Espinosa-Anke L, Ronzano F, Saggion H. Hypernym extraction: combining machine-learning and dependency grammar. In: Gelbukh A, editors. 16th International Conference, CICLing 2015;2015 April 14-20; Cairo, Egypt. Switzerland: Springer Verlag; 2015. p. 372-83. (Lecture Notes in Computer Science; vol. 9041) DOI: 10.1007/978-3-319-18111-0_28978-3-319-18111-00302-9743http://hdl.handle.net/10230/36013Comunicació presentada a la 16th International Conference, (CICLing) celebrada del 14 al 20 d'abril de 2015 a El Caire, Egipte.Hypernym extraction is a crucial task for semantically motivated NLP tasks such as taxonomy and ontology learning, textual entailment or paraphrase identification. In this paper, we describe an approach to hypernym extraction from textual definitions, where machine-learning and post-classification refinement rules are combined. Our best-performing configuration shows competitive results compared to state-of-the-art systems in a well-known benchmarking dataset. The quality of our features is measured by combining them in different feature sets and by ranking them by their Information Gain score. Our experiments confirm that both syntactic and definitional information play a crucial role in the hypernym extraction task.application/pdfeng© Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-18111-0_28info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1007/978-3-319-18111-0_28Parse TreeComputational linguisticsEntity recognitionDependency parsingSemantic role labelinfo:eu-repo/semantics/openAccess