eprintid: 12369 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/01/23/69 datestamp: 2024-05-30 20:51:04 lastmod: 2024-05-30 20:51:05 status_changed: 2024-05-30 20:51:04 type: article metadata_visibility: show creators_name: Khan, Hikmat Ullah creators_name: Anam, Rimsha creators_name: Anwar, Muhammad Waqas creators_name: Jamal, Muhammad Hasan creators_name: Bajwa, Usama Ijaz creators_name: Diez, Isabel de la Torre creators_name: Silva Alvarado, Eduardo René creators_name: Soriano Flores, Emmanuel creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: eduardo.silva@funiber.org creators_id: emmanuel.soriano@uneatlantico.es creators_id: title: A deep learning approach for Named Entity Recognition in Urdu language ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_revistas_cientificas divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public abstract: Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies. date: 2024-03 publication: PLOS ONE volume: 19 number: 3 pagerange: e0300725 id_number: doi:10.1371/journal.pone.0300725 refereed: TRUE issn: 1932-6203 official_url: http://doi.org/10.1371/journal.pone.0300725 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies. metadata Khan, Hikmat Ullah; Anam, Rimsha; Anwar, Muhammad Waqas; Jamal, Muhammad Hasan; Bajwa, Usama Ijaz; Diez, Isabel de la Torre; Silva Alvarado, Eduardo René; Soriano Flores, Emmanuel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.silva@funiber.org, emmanuel.soriano@uneatlantico.es, SIN ESPECIFICAR (2024) A deep learning approach for Named Entity Recognition in Urdu language. PLOS ONE, 19 (3). e0300725. ISSN 1932-6203 document_url: http://repositorio.uneatlantico.es/id/eprint/12369/1/journal.pone.0300725.pdf