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Lemmatization is a Natural Language Processing (NLP) technique used tonormalize text by changing morphological derivations of words to their rootforms. It is used as a core pre-processing step in many NLP tasks includingtext indexing, information retrieval, and machine learning for NLP, amongothers. This paper pioneers the development of text lemmatization for theSomali language, a low-resource language with very limited or no prioreffective adoption of NLP methods and datasets. We especially develop a lexiconand rule-based lemmatizer for Somali text, which is a starting point for afull-fledged Somali lemmatization system for various NLP tasks. Withconsideration of the language morphological rules, we have developed an initiallexicon of 1247 root words and 7173 derivationally related terms enriched withrules for lemmatizing words not present in the lexicon. We have tested thealgorithm on 120 documents of various lengths including news articles, socialmedia posts, and text messages. Our initial results demonstrate that thealgorithm achieves an accuracy of 57\% for relatively long documents (e.g. fullnews articles), 60.57\% for news article extracts, and high accuracy of 95.87\%for short texts such as social media messages.

Lexicon and Rule-based Word Lemmatization Approach for the Somali Language