TY  - JOUR
VL  - 19
AV  - public
JF  - PLOS ONE
SN  - 1932-6203
UR  - http://doi.org/10.1371/journal.pone.0300725
ID  - uninimx12369
N2  - 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.
TI  - A deep learning approach for Named Entity Recognition in Urdu language
A1  - Khan, Hikmat Ullah
A1  - Anam, Rimsha
A1  - Anwar, Muhammad Waqas
A1  - Jamal, Muhammad Hasan
A1  - Bajwa, Usama Ijaz
A1  - Diez, Isabel de la Torre
A1  - Silva Alvarado, Eduardo René
A1  - Soriano Flores, Emmanuel
A1  - Ashraf, Imran
Y1  - 2024/03//
IS  - 3
ER  -