PDF] Reconciling fine-grained lexical knowledge and coarse-grained ontologies in the representation of near-synonyms

A new model for representing fine-grained lexical knowledge whose basis is the idea of granularity of representation is discussed. A machine translation system must be able to adequately cope with near-synonymy for there are often many slightly different translations available for any given source language word that can each significantly and differently affect the meaning or style of a translated text. Conventional models of lexical knowledge used in natural-language processing systems are inadequate for representing near-synonyms, because they are unable to represent fine-grained lexical knowledge. We will discuss a new model for representing fine-grained lexical knowledge whose basis is the idea of granularity of representation.

PDF) Reconciling Fine-Grained Lexical Knowledge and Coarse-Grained Ontologies in the Representation of Near-Synonyms

A generic metamodel for data extraction and generic ontology population - Yohann Chasseray, Anne-Marie Barthe-Delanoë, Stéphane Négny, Jean-Marc Le Lann, 2022

A generic metamodel for data extraction and generic ontology population - Yohann Chasseray, Anne-Marie Barthe-Delanoë, Stéphane Négny, Jean-Marc Le Lann, 2022

PDF) Experiments on Extracting Knowledge from a Machine-Readable Dictionary of Synonym Differences

Data and Knowledge Evolution

PDF) Distributed engineering of ontologies (diligent)

Data and Knowledge Evolution

Decoding the Information Structure Underlying the Neural Representation of Concepts

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