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Mixing Semantic Networks and Conceptual Vectors : Application to Hyperonymy

Violaine Prince 1 Mathieu Lafourcade 1 
1 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In this paper, we focus on lexical semantics, a key issue in Natural Language Processing (NLP) that tends to converge with conceptual Knowledge Representation (KR) and ontologies. When ontological representation is needed, hyperonymy, the closest approximation to the is-a relation, is at stake. In this paper we describe the principles of our vector model (CVM: Conceptual Vector Model), and show how to account for hyperonymy within the vector-based frame for semantics. We show how hyperonymy diverges from is-a and what measures are more accurate for hyperonymy representation. Our demonstration results in initiating a 'cooperation' process between semantic networks and conceptual vectors. Text automatic rewriting or enhancing, ontology mapping with natural language expressions, are examples of applications that can be derived from the functions defined in this paper.
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Submitted on : Friday, January 5, 2007 - 10:56:30 AM
Last modification on : Friday, August 5, 2022 - 3:03:22 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 8:43:34 PM


  • HAL Id : lirmm-00122839, version 1



Violaine Prince, Mathieu Lafourcade. Mixing Semantic Networks and Conceptual Vectors : Application to Hyperonymy. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Institute of Electrical and Electronics Engineers, 2006, 36 (2), pp.152-160. ⟨lirmm-00122839⟩