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Communication Dans Un Congrès Année : 2015

QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts

Résumé

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.
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Dates et versions

hal-01144344 , version 1 (21-04-2015)

Identifiants

  • HAL Id : hal-01144344 , version 1

Citer

Guillaume Cleuziou, Davide Buscaldi, Vincent Levorato, Gaël Dias, Christine Largeron. QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts. International Workshop on Semantic Evaluation (SEMEVAL 2015), 2015, Denver, United States. ⟨hal-01144344⟩
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