data2lang2vec: Data Driven Typological Features Completion
<|reference_start|>data2lang2vec: Data Driven Typological Features Completion: 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.<|reference_end|>
@article{amirzadeh2024data2lang2vec:,
    title={data2lang2vec: Data Driven Typological Features Completion},
    author={Hamidreza Amirzadeh, Sadegh Jafari, Anika Harju, Rob van der Goot},
    journal={arXiv preprint arXiv:2409.17373},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17373},
    primaryClass={cs.CL}
}