A deep learning approach for Named Entity Recognition in Urdu language

Article Subjects > Engineering <http://repositorio.unib.org/view/subjects/uneat=5Feng.html> Europe University of Atlantic > Research > Scientific Production <http://repositorio.unib.org/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html>
Ibero-american International University > Research > Scientific Production <http://repositorio.unib.org/view/divisions/uninimx=5Fproduccion=5Fcientifica.html>
Ibero-american International University > Research > Articles and Books <http://repositorio.unib.org/view/divisions/uninipr=5Fproduccion=5Fcientifica.html>
Universidad Internacional do Cuanza > Research > Scientific Production <http://repositorio.unib.org/view/divisions/unic=5Fproduccion=5Fcientifica.html>
Fundación Universitaria Internacional de Colombia > Research > Scientific Production <http://repositorio.unib.org/view/divisions/unincol=5Fproduccion=5Fcientifica.html> Open English 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 and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, eduardo.silva@funiber.org, emmanuel.soriano@uneatlantico.es, UNSPECIFIED <http://repositorio.unib.org/id/eprint/12369/1/journal.pone.0300725.pdf> (2024) A deep learning approach for Named Entity Recognition in Urdu language. PLOS ONE, 19 (3). e0300725. ISSN 1932-6203