arXiv:2511.16680v1 Announce Type: new 
Abstract: Despite rapid advances in multilingual natural language processing (NLP), the Bantu language Shona remains under-served in terms of morphological analysis and language-aware tools. This paper presents Shona spaCy, an open-source, rule-based morphological pipeline for Shona built on the spaCy framework. The system combines a curated JSON lexicon with linguistically grounded rules to model noun-class prefixes (Mupanda 1-18), verbal subject concords, tense-aspect markers, ideophones, and clitics, integrating these into token-level annotations for lemma, part-of-speech, and morphological features. The toolkit is available via pip install shona-spacy, with source code at https://github.com/HappymoreMasoka/shona-spacy and a PyPI release at https://pypi.org/project/shona-spacy/0.1.4/. Evaluation on formal and informal Shona corpora yields 90% POS-tagging accuracy and 88% morphological-feature accuracy, while maintaining transparency in its linguistic decisions. By bridging descriptive grammar and computational implementation, Shona spaCy advances NLP accessibility and digital inclusion for Shona speakers and provides a template for morphological analysis tools for other under-resourced Bantu languages.

تم تطوير محلل مورفولوجي جديد مفتوح المصدر للغة الشونا، يسمى شونا سبايسي، باستخدام إطار عمل سبايسي. تتكامل هذه الأداة مع معجم JSON منسق وقواعد لغوية لتحسين تحليل بادئات فئة الأسماء، وتوافقات الفاعل الفعلية، وميزات مورفولوجية أخرى، محققة دقة تصل إلى 90% في تصنيف أجزاء الكلام و88% في الميزات المورفولوجية.

Se ha desarrollado un nuevo analizador morfológico de código abierto para el idioma shona, llamado Shona spaCy, utilizando el marco spaCy. Esta herramienta integra un léxico JSON curado y reglas lingüísticas para mejorar el análisis de los prefijos de clase nominal, las concordancias de sujeto verbal y otras características morfológicas, logrando un 90 % de precisión en el etiquetado de partes del discurso y un 88 % en características morfológicas.

Un nouvel analyseur morphologique open-source pour la langue shona, nommé Shona spaCy, a été développé en utilisant le cadre spaCy. Cet outil intègre un lexique JSON soigneusement élaboré et des règles linguistiques pour améliorer l'analyse des préfixes de classe nominale, des concordances de sujet verbal et d'autres caractéristiques morphologiques, atteignant 90 % de précision dans le balisage des parties du discours et 88 % dans les caractéristiques morphologiques.

A new open-source morphological analyzer for the Shona language, named Shona spaCy, has been developed using the spaCy framework. This tool integrates a curated JSON lexicon and linguistically grounded rules to enhance the analysis of noun-class prefixes, verbal subject concords, and other morphological features, achieving 90% accuracy in part-of-speech tagging and 88% in morphological features.

Shona spaCy: A Morphological Analyzer for an Under-Resourced Bantu Language

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