@inproceedings{amirzadeh-etal-2025-data2lang2vec,
title = "data2lang2vec: Data Driven Typological Features Completion",
author = "Amirzadeh, Hamidreza and
Jafari, Sadegh and
Harju, Anika and
van der Goot, Rob",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.435/",
pages = "6520--6529",
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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<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.</abstract>
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%0 Conference Proceedings
%T data2lang2vec: Data Driven Typological Features Completion
%A Amirzadeh, Hamidreza
%A Jafari, Sadegh
%A Harju, Anika
%A van der Goot, Rob
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F amirzadeh-etal-2025-data2lang2vec
%X 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.
%U https://aclanthology.org/2025.coling-main.435/
%P 6520-6529
Markdown (Informal)
[data2lang2vec: Data Driven Typological Features Completion](https://aclanthology.org/2025.coling-main.435/) (Amirzadeh et al., COLING 2025)
ACL
- Hamidreza Amirzadeh, Sadegh Jafari, Anika Harju, and Rob van der Goot. 2025. data2lang2vec: Data Driven Typological Features Completion. In Proceedings of the 31st International Conference on Computational Linguistics, pages 6520–6529, Abu Dhabi, UAE. Association for Computational Linguistics.