| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000607912 |
| ISSN: | 1405-5546 |
| Autores: | Khoufi, Nabil1 Aloulou, Chafik1 |
| Instituciones: | 1University of Sfax, Sfax. Túnez |
| Año: | 2024 |
| Periodo: | Abr-Jun |
| Volumen: | 28 |
| Número: | 2 |
| Paginación: | 517-527 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | The task of chunking involves dividing a sentence into smaller phrases by identifying a limited amount of syntactic information. This process involves grouping together consecutive words to form phrases, also known as shallow parsing. Chunking does not provide information on the relationships between these phrases. This paper describes our approach to building chunking models for Arabic text using deep learning techniques. We evaluated several training models and compared their results using a rich data set. The results we obtained were highly encouraging when compared to previous related studies. |
| Keyword: | NLP, Arabic language, Shallow parsing, Chunking, Deep learning, GRU, LSTM, BILSTM, ATB, Penn Arabic treebank |
| Texto completo: | Texto completo (Ver PDF) Texto completo (Ver HTML) |
