This paper introduces a new non-statistical Arabic lemmatizer algorithm designed for information retrieval systems. The lemmatizer leverages Arabic language knowledge resources to generate accurate lemma forms and relevant features. The algorithm achieves a maximum accuracy of 94.8% and 89.15% on first seen documents, outperforming the Stanford Arabic model's 76.7% on the same dataset. Why it matters: Accurate Arabic lemmatization is crucial for improving the performance of Arabic information retrieval systems, which can enhance access to Arabic language content.
The paper introduces AraToken, an Arabic-optimized tokenizer based on the SentencePiece Unigram algorithm that incorporates a normalization pipeline to handle Arabic-specific orthographic variations. Experiments show that AraToken achieves 18% lower fertility compared to unnormalized baselines. The Language Extension Pipeline (LEP) is introduced to integrate AraToken into Qwen3-0.6B, reducing evaluation loss from 8.28 to 2.43 within 800 training steps on 100K Arabic samples. Why it matters: This research provides an efficient tokenizer tailored for Arabic, improving performance of LLMs on Arabic text and benefiting Arabic NLP research by providing released resources.
This paper introduces an enhanced Dense Passage Retrieval (DPR) framework tailored for Arabic text retrieval. The core innovation is an Attentive Relevance Scoring (ARS) mechanism that improves semantic relevance modeling between questions and passages, replacing standard interaction methods. The method integrates pre-trained Arabic language models and architectural refinements, achieving improved retrieval and ranking accuracy for Arabic question answering. Why it matters: This work addresses the underrepresentation of Arabic in NLP research by providing a novel approach and publicly available code to improve Arabic text retrieval, which can benefit various applications like Arabic search engines and question-answering systems.
This paper explores language-independent alternatives to morphological segmentation for Arabic NLP using data-driven sub-word units, characters as a unit of learning, and word embeddings learned using a character CNN. The study evaluates these methods on machine translation and POS tagging tasks. Results show these methods achieve performance close to or surpassing state-of-the-art approaches. Why it matters: By offering simpler, more adaptable segmentation techniques, this research can help improve Arabic NLP applications across diverse domains and dialects.