| Título |
Urdu Named Entity Recognition with Attention Bi-LSTM-CRF Model |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
21st Mexican International Conference on Artificial Intelligence, MICAI 2022 |
| Resumen |
The named entity recognition (NER) task is a challenging problem in natural language processing (NLP), especially for languages with very few annotated corpora such as Urdu. In this paper we proposed an Attention-Bi-LSTM-CRF method and applied it to the MK-PUCIT Corpus which is the latest NER dataset available for the Urdu language. In addition to word-level embedding, we used an embedding-level focus mechanism. The output of the embedding layer was fed into a bidirectional-LSTM encoder unit, accompanied by another self-attention layer to boost the system’s accuracy. Our Attention-Bi-LSTM-CRF model demonstrated an F1-score of 92%. The cumulative findings of the experiments show that our approach outperforms existing methods, thus yielding a new UNER (Urdu Named Entity Recognition) state-of-the-art performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Observaciones |
DOI 10.1007/978-3-031-19496-2_1
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13613 |
| Lugar |
Monterrey |
| País |
Mexico |
| No. de páginas |
3-17 |
| Vol. / Cap. |
|
| Inicio |
2022-10-24 |
| Fin |
2022-10-29 |
| ISBN/ISSN |
9783031194955 |
