TALN-UPF: taxonomy learning exploiting CRF-based hypernym extraction on encyclopedic definitions

This paper describes the system submitted by the TALN-UPF team to SEMEVAL Task 17 (Taxonomy Extraction Evaluation). We present a method for automatically learning a taxonomy from a flat terminology, which benefits from a definition corpus obtained by querying the BabelNet semantic network. Then, we combine a machine-learning algorithm for term-hypernym extraction with linguistically-motivated heuristics for hypernym decomposition. Our approach performs well in terms of vertex coverage and newly added vertices, while it shows room for improvement in terms of graph topology, edge coverage and precision of novel edges.Paper presented at 9th International Workshop on Semantic Evaluation (SemEval 2015);2015 June 4-5; Denver, Colorado.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 project Dr. Inventor (FP7-ICT-2013.8.1 611383)