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        <title>Lemmatization as a Classification Task: Results from Arabic across Multiple Genres</title>
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    <abstract>Lemmatization is crucial for NLP tasks in morphologically rich languages with ambiguous orthography like Arabic, but existing tools face challenges due to inconsistent standards and limited genre coverage. This paper introduces two novel approaches that frame lemmatization as classification into a Lemma-POS-Gloss (LPG) tagset, leveraging machine translation and semantic clustering. We also present a new Arabic lemmatization test set covering diverse genres, standardized alongside existing datasets. We evaluate character-level sequence-to-sequence models, which perform competitively and offer complementary value, but are limited to lemma prediction (not LPG) and prone to hallucinating implausible forms. Our results show that classification and clustering yield more robust, interpretable outputs, setting new benchmarks for Arabic lemmatization.</abstract>
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%0 Conference Proceedings
%T Lemmatization as a Classification Task: Results from Arabic across Multiple Genres
%A Saeed, Mostafa
%A Habash, Nizar
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F saeed-habash-2025-lemmatization
%X Lemmatization is crucial for NLP tasks in morphologically rich languages with ambiguous orthography like Arabic, but existing tools face challenges due to inconsistent standards and limited genre coverage. This paper introduces two novel approaches that frame lemmatization as classification into a Lemma-POS-Gloss (LPG) tagset, leveraging machine translation and semantic clustering. We also present a new Arabic lemmatization test set covering diverse genres, standardized alongside existing datasets. We evaluate character-level sequence-to-sequence models, which perform competitively and offer complementary value, but are limited to lemma prediction (not LPG) and prone to hallucinating implausible forms. Our results show that classification and clustering yield more robust, interpretable outputs, setting new benchmarks for Arabic lemmatization.
%R 10.18653/v1/2025.emnlp-main.1525
%U https://aclanthology.org/2025.emnlp-main.1525/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1525
%P 30014-30029

Markdown (Informal)

[Lemmatization as a Classification Task: Results from Arabic across Multiple Genres](https://aclanthology.org/2025.emnlp-main.1525/) (Saeed & Habash, EMNLP 2025)

• Lemmatization as a Classification Task: Results from Arabic across Multiple Genres (Saeed & Habash, EMNLP 2025)

ACL

• Mostafa Saeed and Nizar Habash. 2025. Lemmatization as a Classification Task: Results from Arabic across Multiple Genres. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 30014–30029, Suzhou, China. Association for Computational Linguistics.