data2lang2vec: Data Driven Typological Features Completion

• URL: http://arxiv.org/abs/2409.17373v1
• Date: Wed, 25 Sep 2024 21:32:57 GMT
• Title: data2lang2vec: Data Driven Typological Features Completion
• Authors: Hamidreza Amirzadeh, Sadegh Jafari, Anika Harju, Rob van der Goot,
• Abstract summary: We introduce a multi-lingual Part-of-Speech (POS) tagger, achieving over 70% accuracy across 1,749 languages. We also introduce a more realistic evaluation setup, focusing on likely to be missing typology features.
• Score: 8.28573483085828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language typology databases enhance multi-lingual Natural Language Processing (NLP) by improving model adaptability to diverse linguistic structures. The widely-used lang2vec toolkit integrates several such databases, but its coverage remains limited at 28.9\%. Previous work on automatically increasing coverage predicts missing values based on features from other languages or focuses on single features, we propose to use textual data for better-informed feature prediction. To this end, we introduce a multi-lingual Part-of-Speech (POS) tagger, achieving over 70\% accuracy across 1,749 languages, and experiment with external statistical features and a variety of machine learning algorithms. We also introduce a more realistic evaluation setup, focusing on likely to be missing typology features, and show that our approach outperforms previous work in both setups.

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