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Mixing Semantic Networks and Conceptual Vectors: The Case of 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 con- verge with conceptual Knowledge Representation (KR) and ontologies. When ontological representation is needed, hy- peronymy, the closest approximation to the is-a relation, is at stake. In this paper we describe the principles of our vec- tor 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 rep- resentation. Our demonstration results in initiating a ’coop- eration’ 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 we define in this paper.
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Submitted on : Wednesday, September 7, 2022 - 10:20:07 AM
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Violaine Prince, Mathieu Lafourcade. Mixing Semantic Networks and Conceptual Vectors: The Case of Hyperonymy. ICCI 2003 - 2nd IEEE International Conference on Cognitive Informatics, Aug 2003, London, United Kingdom. pp.121-128, ⟨10.1109/COGINF.2003.1225968⟩. ⟨lirmm-00269569⟩



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