QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts
This paper presents our participation to the SemEval Task 17, related to "Taxonomy Extraction Evaluation" (Bordea et al. 2015) We propose a new methodology for semi supervised and auto supervised acquisition of lexical taxonomies from raw texts. Our approach is based on the theory of pretopology that offers a powerful formalism to model subsumption relations and transforms a list of terms into a structured term space by combining different discriminant criteria. In order to reach a good pretopological space, we define the Learning Pretopological Spaces method that learns a parameterized space by using an evolutionary strategy.
*SEMEVAL