Comunicació 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.

This work is partially funded by the SKATER project, TIN2012-38584- C06-03, Ministerio de Economía y Competitividad, Secretaría de Estado de Investigación, Desarrollo e Innovación, España; and Dr. Inventor (FP7-ICT-2013.8.1 611383), programa Ramón y Cajal 2009 (RYC-2009-04291).