| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560352 |
| ISSN: | 1405-5546 |
| Autores: | Thi, Luong Nguyen1 My, Linh Ha2 Minh, Huyen Nguyen Thi2 Le Hong, Phuong2 |
| Instituciones: | 1Dalat University, Lamdong. Vietnam 2VNU University of Science, Hanoi. Vietnam |
| Año: | 2018 |
| Periodo: | Jul-Sep |
| Volumen: | 22 |
| Número: | 3 |
| Paginación: | 853-862 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Recently, deep learning methods have achieved good results in dependency parsing for many natural languages. In this paper, we investigate the use of bidirectional long short-term memory network models for both transition-based and graph-based dependency parsing for the Vietnamese language. We also report our contribution in building a Vietnamese dependency treebank whose tagset conforms to the Universal Dependency schema. Various experiments demonstrate the efficiency of this method, which achieves the best parsing accuracy in comparison to other existing approaches on the same corpus, with unlabeled attachment score of 84.45% or labeled attachment score of 78.56%. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Deep learning, BiLSTM, Dependency parsing, Vietnamese, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
