AfroScope: New Framework Advances Language Identification for 640 African Languages
Researchers have introduced AfroScope, a comprehensive framework for identifying African languages in text, covering 640 languages with specialized models for distinguishing closely related varieties. The system addresses a critical gap in natural language processing, as existing tools have limited coverage of African languages and struggle to differentiate between similar language variants. This work enables more reliable downstream NLP applications and large-scale analysis of Africa's digital linguistic landscape.
AfroScope comprises three main components: AfroScope-Data (a dataset of 640 African languages), AfroScope-Models (a suite of language identification models), and AfroScope-Mirror (a specialized embedding model for disambiguating confusable languages). The framework uses hierarchical classification to improve accuracy on closely related languages, achieving a 1.57-point improvement in macro-F1 scores on difficult language pairs compared to baseline models. The researchers analyzed how language-family structure, script compatibility, and domain coverage affect identification performance, and released both the dataset and models publicly. This work positions language identification as foundational infrastructure for studying Africa's linguistic diversity in digital text and supporting downstream NLP applications that depend on accurate language detection.
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- arXiv cs.CLCenter
AfroScope: A Framework for Studying the Linguistic Landscape of Africa
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