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Article · International Journal of Basic and Applied Sciences · July 20, 2025

Afaan Oromoo Textual Entailment Classification Using Deep Learning Approach

Diro Tolosa, Arulmurugan Ramu, Ramata Mosissa, Teshome Debushe, Desalegn Tasew, Diriba Gichile Rundasa

Arulmurugan Ramu
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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.

Topic ModelingNatural Language Processing TechniquesSentiment Analysis and Opinion MiningAfaan Oromoolow-resource languagesconvolutional neural networks

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