Afaan Oromoo Textual Entailment Classification Using Deep Learning Approach

  • Authors

    • Diro Tolosa Department of Educational Technology and Information Management, Mattu University
    • Arulmurugan Ramu Department of Computational Sciences and Software Engineering, Heriot-Watt International Faculty, K. Zhubanov Aktobe Regional ‎University
    • Ramata Mosissa Department of Information Technology, Mattu University
    • Teshome Debushe Department of Information Technology, Mattu University
    • Desalegn Tasew Department of Electrical and Computer Engineering, Mattu University
    • Diriba Gichile Department of Computer Science, Mattu University
    https://doi.org/10.14419/vnjbkz65

    Received date: June 7, 2025

    Accepted date: July 15, 2025

    Published date: July 20, 2025

  • Afaan Oromoo; Deep Learning; Natural Language Processing; Textual Entailment Classification

  • Abstract

    Natural language processing (NLP) is the field that enables computers to understand and use human language. Textual entailment—a key ‎NLP task— determines if a hypothesis can logically follow from a given premise. As we reviewed, the model designed and developed for ‎other languages is not used for Afaan Oromoo textual entailment classification, as its semantics and syntax are different when compared with ‎other languages. To address the gap, we proposed an Afaan Oromoo textual entailment classification model. We used Support Vector Machine ‎‎(SVM) as a baseline to compare with three deep learning architectures: Convolutional Neural Network (CNN), Long Short-Term Memory ‎‎(LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) by comparing their performance to identify the most effective approach ‎with fasttext and word2vec word embedding. We collected a dataset of 13,060 sentence pairs in Afaan Oromoo. The accuracy of SVM was ‎‎55.82% and the accuracy of CNN, LSTM, and BiLSTM was 72.8%, 75.57% and 80.47% respectively, with fasttext word embedding. ‎Considering the limited resources available for Afaan Oromoo NLP, the result is encouraging. As a starting point, this study offers a basis for ‎additional investigation and advancement in this field and contributes to the development of Afaan Oromoo's Natural Language Processing ‎capabilities‎.

  • References

    1. R. Navigli and V. R. Elena, “Natural Language Understanding : Instructions for ( Present and Future ) Use,” pp. 5697–5702, 2003. https://doi.org/10.24963/ijcai.2018/812.
    2. K. M. Verspoor and K. B. Cohen, “Synonyms,” no. June, 2018,
    3. M. H. Haggag and A. Mohammed, “Different Models and Approaches of Textual Entailment Recognition,” vol. 142, no. 1, pp. 32–39, 2016. https://doi.org/10.5120/ijca2016909667.
    4. A. Poliak, “A Survey on Recognizing Textual Entailment as an NLP Evaluation”.
    5. A. Mishra, “Deep Learning Techniques in Textual Entailment”.
    6. L. Bentivogli, I. Dagan, H. T. Dang, D. Giampiccolo, B. Magnini, and R. Gan, “The Fifth PASCAL Recognizing Textual Entailment Challenge”.
    7. R. Gan, L. P. Group, O. M. Way, H. Language, and F. B. Kessler, “Recognizing textual entailment : Rational , evaluation and approaches,” vol. 15, no. 4, 2009, https://doi.org/10.1017/S1351324909990209.
    8. P. Eleftheriadis, I. Perikos, and I. Hatzilygeroudis, “Evaluating Deep Learning Techniques for Natural Language Inference,” Appl. Sci., vol. 13, no. 4, 2023, https://doi.org/10.3390/app13042577.
    9. E. T. Garoma, “Demonstratives in Afaan Oromoo,” Cogent Arts Humanit., vol. 11, no. 1, p., 2024, https://doi.org/10.1080/23311983.2023.2297494.
    10. H. Mesfin, “College of Natural Sciences Helina Mesfin Advisor : Fekade Getahun ( PhD ),” 2020.
    11. M. R. Costa-jussà, “Recognizing Textual Entailment using Deep Learning Techniques Supervisor :,” 2017.
    12. C. Lyu, Y. Lu, D. Ji, and B. Chen, “Deep Learning for Textual Entailment Recognition,” 2015, https://doi.org/10.1109/ICTAI.2015.35.
    13. M. H. Haggag and A. M. Ahmed, “Recognizing Textual Entailment based on Deep Learning Approach,” vol. 181, no. 43, pp. 36–41, 2019.
    14. P. Pakray, S. Bandyopadhyay, and A. Gelbukh, “A Hybrid Textual Entailment System using Lexical and Syntactic Features”. https://doi.org/10.5120/ijca2019918515.
    15. D. Rudrapal, A. Das, and B. Bhattacharya, “Recognition of Partial Textual Entailment for Indian Social Media Text,” vol. 23, no. 1, pp. 143–152, 2019, https://doi.org/10.13053/cys-23-1-2816.
    16. Q. Nhat and M. Pham, “A Machine Learning based Textual Entailment Recognition System of JAIST Team for NTCIR9 RITE”.
    17. M. Khader, A. Awajan, and A. Alkouz, “Textual Entailment for Arabic Language based on Lexical and Semantic Matching,” no. October, 2016, https://doi.org/10.21700/ijcis.2016.109.
    18. N. Almarwani and M. Diab, “Arabic Textual Entailment with Word Embeddings,” pp. 185–190, 2017. https://doi.org/10.18653/v1/W17-1322.
    19. M. B. W. Bakari and M. Neji, “Recognizing Textual Entailment for Arabic using seman- tic similarity and Word Sense Disambiguation,” vol. 3, no. 1, pp. 1–10.

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  • How to Cite

    Tolosa, D., Ramu, A. ., Mosissa , R. ., Debushe , T. ., Tasew , D. ., & Gichile, D. . (2025). Afaan Oromoo Textual Entailment Classification Using Deep Learning Approach. International Journal of Basic and Applied Sciences, 14(3), 150-155. https://doi.org/10.14419/vnjbkz65