School of Information Technology and Engineering

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Browsing School of Information Technology and Engineering by Subject "Amharic"

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    Advancing Amharic Text Summarization with a Tailored Parameter-Efficient Fine-Tuning Technique
    (Addis Ababa University, 2025-08) Dagim Melkie; Fantahun Bogale (PhD)
    While recent progress in Large Language Models (LLMs) has revolutionized the field of Natural Language Processing (NLP), applying these models to low-resource languages such as Amharic presents considerable difficulties. Key obstacles include the scarcity of available data and the intensive computational cost associated with conventional finetuning methods. To overcome these issues, this thesis introduces a specialized parameterefficient fine-tuning (PEFT) framework developed specifically for Amharic text summarization. This new framework combines a dynamic low-rank adaptation component (DyLoRA-Amharic) with an adaptive activation method (AdaptAmharic), which work together to improve the model’s flexibility and optimize its resource allocation during training. The methodology involves injecting these custom modules into the mT5-small encoder– decoder architecture, allowing dynamic adjustment of DyLoRA-Amharic ranks and AdaptAmharic activation levels based on gradient signals. A joint optimization objective incorporating regularization terms for both rank and activation was employed to manage model complexity and ensure training stability. Comparative experiments were conducted against standard PEFT LoRA and Houlsby Adapter baselines on a curated Amharic summarization dataset. Experimental results demonstrate that the proposed DyLoRA-Amharic and Adapt Amharic framework significantly out performs the baselines across ROUGE, BLEU, and BERT Score metrics, achieving the lowest evaluation loss. Specifically, it improved ROUGEL by 30.5% and BLEU by 52.4% over the strongest baseline. This superior performance validates the efficacy of a densely injected, dynamic, and regularized architecture, challenging the conventional emphasis on maximal sparsity in PEFT. While the framework utilizes a higher proportion of trainable parameters (13.42%) compared to the baselines, this trade-off is justified by the substantial performance gains. This research contributes to advancing PEFT methodologies for low-resource NLP, providing a robust and adaptable solution for Amharic text summarization. The findings lay a foundation for developing more efficient and effective LLMs for diverse and linguistically underrepresented communities
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    Deep Learning-Based Amharic Keyword Extraction for Open-Source Intelligence Analysis
    (Addis Ababa Univeristy, 2025-06) Alemayehu Gutema; Henok Mulugeta (PhD)
    In today's digital age, the problem of information overload has become a pressing concern, especially in the field of OSINT (Open-Source Intelligence). With vast amounts of data available on the internet, it is challenging to separate relevant and credible information from the noise. An OSINT approach involves gathering intelligence from publicly available sources. However, with the increasing volume and diversity of online content, it has become difficult to extract actionable intelligence from enormous amounts of data. Deep learning can help identify patterns in large amounts of data and automate decision-making processes. Despite these advances, a problem of information overload still exists. One approach to addressing this problem is to develop effective deep learning model to extract the relevant information. Leveraging both machine and deep learning algorithms with natural language processing (NLP) can help automatically classify and categorize information. The purpose of this study is to design deep learning model to extract intelligence from vast amount of Amharic dataset, aiming to design model for keyword extraction. Keyword extraction is the process of identifying important words or phrases that capture the essence of a given piece of text. This task is critical for many natural language processing applications, including document summarization, information retrieval, and search engine optimization. In recent years, deep learning algorithms have shown great promise in this field, largely due to their ability to learn from vast amounts of data and extract complex patterns. In this paper, we propose a novel keyword extraction approach based on deep learning methods. We will explore different algorithms, such as recurrent neural networks (RNNs) and transformer models, to learn the relevant features from the input text and predict the most salient keywords. We evaluate our proposed method on datasets containing Amharic content, and show that it outperforms state-of-the-art methods. Our results suggest that deep learning-based approaches have the potential to significantly improve keyword extraction accuracy and scalability in realworld application.