• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 47, Pages: 2612-2618

Original Article

Named Entity Recognition for Hadiyya Language using BiLSTM-CRF Model

Received Date:19 May 2022, Accepted Date:10 September 2022, Published Date:21 December 2022


Objective: This work aims to the development of Hadiyya language named entity recognition which is widely used in text summarization, machine translation, and information retrieval to categorizing and predicting tokens of a given corpus into predefined named entity classes Method : In this paper, a method combining Bidirectional Long Short-Term Memory neural network with Conditional Random Field (BiLSTM-CRF) is proposed to automatically recognize entities of Hadiyya language (Location, time, person, geography and other nonname entity) from annotated Hadiyya language corpus, the experiment in this work was conducted to discover the most suitable features for Hadiyya NER system. We have collected the data from Department of Hadiya Language & Literature (DHLL) at Wachemo University, Ethiopia. Hadiyya TV, and Hadiyya Media Network (HMN) Therefore, a newly annotated dataset having 5,148 instances is used for this study. We have used 70 % for training and 30% for testing Hadiyya NER system. Finding: after training and validating BiLSTM-CRF model using the collected dataset we have obtained a result of precision, recall and f1-measure values of 95.49%, 94.93%, and 95.21% respectively. Novelty: Finally, we have contributed by hybrid NER system in Hadiyya language to obtain state-of-the-art result which is independent of other natural language processing tasks.

Keywords: Conditional Random Forest; Hadiyya Language; Long Short-Term Memory; Hadiyya Media Network


  1. Patil N, Patil A, Pawar BV. Named Entity Recognition using Conditional Random Fields. Procedia Computer Science. 2020;167:1181–1188. Available from: https://doi.org/10.1016/j.procs.2020.03.431
  2. Gardie B, Solomon Z. Afan-Oromo Named Entity Recognition Using Bidirectional RNN. Indian Journal of Science and Technology. 2022;15(16):736–741. Available from: https://doi.org/10.17485/IJST/v15i16.123
  3. Abafogi A. Boosting Afaan Oromo Named Entity Recognition with Multiple Methods. Int. J. Inf. Eng. Electron. Bus. 2021;13(5):51–59. Available from: http://dx.doi.org/10.5815/ijieeb.2021.05.05
  4. Xu H, Hu B. Legal Text Recognition Using LSTM-CRF Deep Learning Model. Computational Intelligence and Neuroscience. 2022;2022:1–10. Available from: https://doi.org/10.1155/2022/9933929
  5. Deng N, Fu H, Chen X. Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM-CRF. Wireless Communications and Mobile Computing. 2021;2021(1):1–12. Available from: https://doi.org/10.1155/2021/6696205
  6. Wei H, Gao M, Zhou A, Chen F, Qu W, Wang C, et al. Named Entity Recognition From Biomedical Texts Using a Fusion Attention-Based BiLSTM-CRF. IEEE Access. 2019;7:73627–73636. Available from: https://doi:10.1109/ACCESS.2019.2920734
  7. Elfaik H, Nfaoui EH. Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic Text. Journal of Intelligent Systems. 2020;30(1):395–412. Available from: https://doi.org/10.1515/jisys-2020-0021
  8. Huang W, Hu D, Deng Z, Nie J. Named entity recognition for Chinese judgment documents based on BiLSTM and CRF. EURASIP Journal on Image and Video Processing. 2020;2020. Available from: https://doi.org/10.1186/s13640-020-00539-x
  9. Gardie B, Asemie S, Azezew K. Anyuak Language Named Entity Recognition Using Deep Learning Approach. Indian Journal of Science and Technology. 2021;14(39):2998–3006. Available from: https://doi.org/10.17485/IJST/v14i39.1163
  10. Wu G, Tang G, Wang Z, Zhang Z, Wang Z. An Attention-Based BiLSTM-CRF Model for Chinese Clinic Named Entity Recognition. IEEE Access. 2019;7:113942–113949. Available from: https://doi.org/10.1109/ACCESS.2019.2935223
  11. Cho M, Ha J, Park C, Park S. Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition. Journal of Biomedical Informatics. 2020;103:103381. Available from: https://doi.org/10.1016/j.jbi.2020.103381
  12. Han X, Zhou F, Hao Z, Liu Q, Li Y, Qin Q. MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion. Complexity. 2021;2021(2):1–9. Available from: https://doi.org/10.1155/2021/6696064
  13. Muralikrishna H, Sapra P, Jain A, Dinesh DA. Spoken Language Identification Using Bidirectional LSTM Based LID Sequential Senones. 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). 2019;p. 320–326. Available from: https://doi:10.1109/ASRU46091.2019.9003947


© 2022 Ashebir & Tadesse.  This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.