Lemmatization, which reduces words to their root forms, plays a key role in tasks such as information retrieval, text indexing, and machinelearning-based language models. However, a key research challenge for low-resourced languages such as the Somali is the lack of human-annotated lemmatization datasets and reliable ground truth to underpin accurate morphological analysis and training relevant NLP models. To address this problem, we developed the first large-scale, purpose-built Somali lemmatization lexicon, coupled with a crowdsourcing platform for ongoing expansion. The system leverages Somali’s agglutinative and derivational morphology, encompassing over5,584 root words and 78,629 derivative forms, each annotated with part-of-speech tags. For data validation purpose, we have devised a pilot lexicon-based lemmatizer integrated with rule-based logic to handle out-of-vocabulary terms. Evaluation on a 294-document corpuscovering news articles, social media posts, and short messages shows lemmatization accuracies of 51.27% for full articles, 44.14% forexcerpts, and 59.51% for short texts such as tweets. These results demonstrate that combining lexical resources, POS tagging, and rulebased strategies provides a robust and scalable framework for addressing morphological complexity in Somali and other low-resource languages

Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments. Learned metrics such as COMET have higher correlation; however, the lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET: COMET evaluation metrics for African languages by leveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-the-art MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441).