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Curating global datasets of structural linguistic features for independence.
Anna Graff1,2, Natalia Chousou-Polydouri3,4, David Inman3
1Institute for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Zürich, Switzerland. anna.graff@uzh.ch.
Researchers created new global linguistic datasets by curating existing data. These datasets minimize feature dependencies, enabling robust analyses of structural linguistic diversity and language evolution.
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Area of Science:
- Linguistics
- Computational Linguistics
- Evolutionary Linguistics
Background:
- Cross-linguistic databases are crucial for studying language evolution and human history.
- Existing databases often lack feature independence, invalidating large-scale statistical analyses.
- This limits the reliable use of linguistic data for comparative studies.
Purpose of the Study:
- To curate existing linguistic data into new, statistically sound global datasets.
- To address the issue of feature independence in cross-linguistic data.
- To enable more robust analyses of global linguistic diversity.
Main Methods:
- Curated data from five major linguistic databases (Grambank, WALS, AUTOTYP, PHOIBLE, Lexibank).
- Generated two global cross-linguistic datasets (GBI and TLI) minimizing feature dependencies.
- Densified datasets to reduce missing data and ensure reusability.
Main Results:
- Developed two global cross-linguistic datasets (GBI and TLI) with minimized feature dependencies.
- The curated datasets reveal robust and comparable global patterns of structural linguistic diversity.
- The methodology ensures reusability for future linguistic data curation.
Conclusions:
- The newly curated datasets provide a statistically valid resource for large-scale linguistic analyses.
- These datasets facilitate deeper insights into language evolution and human history.
- The framework supports the creation of independent feature datasets from various sources.

