Machine Translation of polysemic words: current technology in light of Cognitive Linguistics
In view of the constant improvement of machine translation technologies today and their use by students and additional language learners, this paper aims to investigate how free online Machine Translators (MTs) work with polysemic words undoing the ambiguity of meanings in the light of the theoretic...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Institución: | Universidade Federal do Ceará (UFC) |
| Repositorio: | Entrepalavras |
| Idioma: | portugués |
| OAI Identifier: | oai:ojs.localhost:article/2173 |
| Acceso en línea: | http://www.entrepalavras.ufc.br/revista/index.php/Revista/article/view/2173 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine translation. Polysemy. Cognitive Linguistics. Lexical semantics. Homonymy. Tradução automática. Polissemia. Linguística Cognitiva. Semântica lexical. Homonímia. |
| Sumario: | In view of the constant improvement of machine translation technologies today and their use by students and additional language learners, this paper aims to investigate how free online Machine Translators (MTs) work with polysemic words undoing the ambiguity of meanings in the light of the theoretical approach of Cognitive Linguistics (CL). The study of the MTs functioning shows that these digital technologies have become increasingly sophisticated due to corpus-based systems and, more recently, the neural system. However, the MT systems still have limitations related to linguistic issues, among them, ambiguity, which can be described as one of the biggest challenges for MT nowadays. In this qualitative study, we analyzed comparatively the results of Portuguese-English automatic translations of polysemic words inserted in sentences with different contexts of use, comparing four free MTs available on the internet: (1) Google Translate, (2) Reverso, (3) Collis and (4) Wordlingo. The results indicate the importance of specifying the context for word disambiguation in machine translation. |
|---|