Named Entity Recognition for Hadiyya Language using BiLSTM-CRF Model

Desalegn Ashebir; Getahun Tadesse

doi:10.17485/IJST/v15i47.1090

Article

Named Entity Recognition for Hadiyya Language using BiLSTM-CRF Model

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i47.1090

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

Original Article

Named Entity Recognition for Hadiyya Language using BiLSTM-CRF Model

Desalegn Ashebir¹, Getahun Tadesse^2*

¹Department of Software Engineering, Mizan Tepi University, Ethiopia
²Department of Computer Science, Mizan Tepi University, Ethiopia

*Corresponding Author
Email: [email protected]

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

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

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

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Copyright

© 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)