A Methodology for Large-Scale, Disambiguated and Unbiased Lexical Knowledge Acquisition Based on Multilingual Word Alignment
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20 octobre 2022
- doi: 10.4000/books.aaccademia.10653
- eisbn: 9791280136947
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
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linguistica computazionale linguistica linguistique computationelle linguistique Computational Linguistics Language Linguistics LAN009000 Linguistics CFSujets proches
Philosophy--Methodology Linguistic science Science of languageCiter ce document
Francesca Grasso et al., « A Methodology for Large-Scale, Disambiguated and Unbiased Lexical Knowledge Acquisition Based on Multilingual Word Alignment », Accademia University Press, ID : 10.4000/books.aaccademia.10653
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Résumé
In order to be concretely effective, many NLP applications require the availability of lexical resources providing varied, broadly shared, and language-unbounded lexical information. However, state-of-the-art knowledge models rarely adopt such a comprehensive and cross-lingual approach to semantics. In this paper, we propose a novel automatable methodology for knowledge modeling based on a multilingual word alignment mechanism that enhances the encoding of unbiased and naturally disambiguated lexical knowledge. Results from a simple implementation of the proposal show relevant outcomes that are not found in other resources.