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Communication Dans Un Congrès Année : 2015

Learning Pretopological Spaces for Lexical Taxonomy Acquisition

Résumé

In this paper, we propose a new methodology for semi-supervised acquisition of lexical taxonomies from a list of existing terms. Our approach is based on the theory of pretopology that offers a powerful formalism to model semantic relations and transform a list of terms into a structured term space by combining different discriminant criteria. In order to learn a parameterized pretopological space, we define the Learning Pretopological Spaces strategy based on genetic algorithms. The rare but accurate pieces of knowledge given by an expert (semi-supervision) or automatically extracted with existing linguistic patterns (auto-supervision) are used to parameterize the different features defining the pretopological term space. Then, a structuring algorithm is used to transform the pretopological space into a lexical taxonomy, i.e. a direct acyclic graph. Results over three standard datasets (two from WordNet and one from UMLS) evidence improved performances against existing associative and pattern-based state-of-the-art approaches.
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Dates et versions

hal-01163702 , version 1 (15-06-2015)

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  • HAL Id : hal-01163702 , version 1

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Guillaume Cleuziou, Gaël Dias. Learning Pretopological Spaces for Lexical Taxonomy Acquisition. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2015, Porto, Portugal. ⟨hal-01163702⟩
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