• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2021, Volume: 14, Issue: 39, Pages: 2998-3006

Original Article

Anyuak Language Named Entity Recognition Using Deep Learning Approach

Received Date:24 June 2021, Accepted Date:17 October 2021, Published Date:27 November 2021


Objectives: This study aims about the development of Anyuak language named entity recognition of its first kind. NER is a fundamental sub task in natural language processing and the high accuracy competence in NER system marks the effectiveness of the downstream tasks. Anyuak language named entity recognition concern is addressed by using a long short-term memory model to categorize tokens into predefined classes. Methods: A long short-term memory is used to model the NER for Anyuak language to detect and classify words into five predefined classes: Person, Time, Organization, Location, and Others (non-named entity words). Because of feature selection plays a vital role in long short-term memory framework, the experiment in this work were conducted to discover most suitable features for Anyuak NER tagging task. Findings: When we evaluated the experiment in cross-validation, we achieved a promising result of precision, recall, and F1-measure values of 98%, 90, and 94% respectively. From the experimental result, it is possible to determine that tag context, word features, part of speech tags, suffixes and prefixes are significant features in named entity recognition and classification for Anyuak language. Novelty: Finally we have contributed a new architecture for Anyuak NER which uses automatically features for Anyuak named entity recognition which are not dependent on other NLP tasks. We proved that deep learning models can be extended, trained and can work for Anuak languages.

Keywords: Named entity recognition in Anyuak; Recurrent neural network; long shortterm memory; Natural language processing; and deep learning


  1. Maraisa CH, Jose AGdS, Luiz CG, Emilio GA, Antonio CdO, Fernando IFdC. Correlations between chemistry components of caryopsis in oat genotypes cultivated in different environments. African Journal of Agricultural Research. 2015;10:4295–4305. Available from: https://dx.doi.org/10.5897/ajar2015.10079
  2. Gamback B, Sikdar UK. Named entity recognition for Amharic using deep learning. 2017 IST-Africa Week Conference (IST-Africa). 2017. doi: 10.23919/ISTAFRICA.2017.8102402
  3. Küçük D, Jacquet G, Steinberger R. Named entity recognition on Turkish tweets. Proc. 9th Int. Conf. Lang. Resour. Eval. Lr. 2014;p. 450–454.
  4. 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://dx.doi.org/10.1155/2021/6696205
  5. 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://dx.doi.org/10.1155/2021/6696064
  6. Collobert R, Weston J, Com J, Karlen M, Kavukcuoglu K, Kuksa P. Natural Language Processing (Almost) from Scratch. 2011. doi: 10.5555/1953048.2078186
  7. Chiu JPC, Nichols E. Named Entity Recognition with Bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics. 2016;4:357–370. Available from: https://dx.doi.org/10.1162/tacl_a_00104
  8. Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF Models for Sequence Tagging. arxiv. 2015. Available from: https://arxiv.org/abs/1508.01991


© 2021 Gardie et al. 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.