| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560716 |
| ISSN: | 1405-5546 |
| Autores: | Bakarov, Amir1 |
| Instituciones: | 1National Research University Higher School of Economics, Moscow. Rusia |
| Año: | 2022 |
| Periodo: | Jul-Sep |
| Volumen: | 26 |
| Número: | 3 |
| Paginación: | 1343-1364 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Distributional Semantics Models are one of the most ubiquitous tools in Natural Language Processing. However, it is still unclear how to optimize such models for specific tasks and how to evaluate them in a general setting (having ability to be successfully applied to any language task in mind). We argue that benefits of intrinsic distributional semantic models evaluation could be questioned since the notion of their “general quality” possibly does not exist; distributional semantic models, however, can be considered as a part of Semantic Maps framework which formalizes the notion of linguistic representativeness on the lexical level. |
| Disciplinas: | Ciencias de la computación, Literatura y lingüística |
| Palabras clave: | Inteligencia artificial, Lingüística aplicada |
| Keyword: | Artificial intelligence, Applied linguistics |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
