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.

Diversicon: Pluggable Lexical Domain Knowledge

Enabling Web-scale data integration in biomedicine through Linked Open Data

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

XI ICE - International Congress of Egyptologist

Ontology Learning Using Word Net Lexical Expansion and Text Mining

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] Near-synonymy and the structure of lexical knowledge

Mining the Web: Discovering Knowledge from Hypertext Data by Safia Abdalla - Issuu