| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560735 |
| ISSN: | 1405-5546 |
| Autores: | Quang Nhat Minh, Pham1 |
| Instituciones: | 1Alt Vietnam Co, Hanoi. Vietnam |
| Año: | 2022 |
| Periodo: | Jul-Sep |
| Volumen: | 26 |
| Número: | 3 |
| Paginación: | 1323-1331 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | In this paper, we present a feature-based named entity recognition (NER) model that achieves the start-of-the-art accuracy for Vietnamese language. We combine word, word-shape features, PoS, chunk, Brown-cluster-based features, and word-embedding-based features in the Conditional Random Fields (CRF) model. We also explore the effects of word segmentation, PoS tagging, and chunking results of many popular Vietnamese NLP toolkits on the accuracy of the proposed feature-based NER model. Up to now, our work is the first work that systematically performs an extrinsic evaluation of basic Vietnamese NLP toolkits on the downstream NER task. Experimental results show that while automatically-generated word segmentation is useful, PoS and chunking information generated by Vietnamese NLP tools does not show their benefits for the proposed feature-based NER model. |
| Disciplinas: | Literatura y lingüística, Ciencias de la computación |
| Palabras clave: | Lingüística aplicada, Programación |
| Keyword: | Applied linguistics, Programming |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
