| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560432 |
| ISSN: | 1405-5546 |
| Autores: | Akdemir, Arda1 Güngör, Tunga2 |
| Instituciones: | 1University of Tokyo, Tokyo, Kanto. Japón 2Bogaziçi University, Istanbul. Turquía |
| Año: | 2019 |
| Periodo: | Jul-Sep |
| Volumen: | 23 |
| Número: | 3 |
| Paginación: | 841-850 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Joint learning of different NLP-related tasks is an emerging research field in Machine Learning. Yet, most of the recent models proposed on joint learning require a dataset that is annotated jointly for all the tasks involved. Such datasets are available only for frequently used languages. In this paper, we propose a novel BiLSTM CRF based joint learning model for dependency parsing and named entity recognition tasks, which has not been employed before for Turkish to the best of our knowledge. This enables joint learning of various tasks for languages that have limited amount of annotated datasets. Our model, tested on a frequently used NER dataset for Turkish, has comparable results with the state-of-the-art systems. We also show that our proposed model outperforms the joint learning model which uses a single dataset. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Joint learning, Named entity recognition, Dependency parsing, Turkish, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
