QASSIT at SemEval-2016 Task 13: On the Integration of Semantic Vectors in Pretopological Spaces for Lexical Taxonomy Acquisition

This paper presents our participation to the SemEval “Task 13: Taxonomy Extraction Evaluation (TExEval-2)” (Bordea et al., 2016). This year, we propose the combination of recent semantic vectors representation into a methodology for semisupervised and auto-supervised acquisition of lexical taxonomies from raw texts. In our proposal, first similarities between concepts are calculated using semantic vectors, then a pretopological space is defined from which a preliminary structure is constructed. Finally, a genetic algorithm is used to optimize two different functions, the quality of the added relationships in the taxonomy and the quality of the structure. Experiments show that our proposal has a competitive performance when compared with the other participants achieving the second position in the general rank.