Extending dependencies to the taggedPBC: Word order in transitive clauses

TL;DR

This paper enhances the taggedPBC dataset by adding dependency annotations, enabling crosslinguistic analysis of word order in transitive clauses, and demonstrates its usefulness despite data noise.

Contribution

It provides a dependency-annotated version of the large taggedPBC dataset, facilitating typological research on word order across many languages.

Findings

Dependency-based word order correlates with expert typological data

Annotated corpus is useful despite noisy tags

Dataset is publicly available for research

Abstract

The taggedPBC (Ring 2025a) contains more than 1,800 sentences of pos-tagged parallel text data from over 1,500 languages, representing 133 language families and 111 isolates. While this dwarfs previously available resources, and the POS tags achieve decent accuracy, allowing for predictive crosslinguistic insights (Ring 2025b), the dataset was not initially annotated for dependencies. This paper reports on a CoNLLU-formatted version of the dataset which transfers dependency information along with POS tags to all languages in the taggedPBC. Although there are various concerns regarding the quality of the tags and the dependencies, word order information derived from this dataset regarding the position of arguments and predicates in transitive clauses correlates with expert determinations of word order in three typological databases (WALS, Grambank, Autotyp). This highlights the usefulness of corpus-based typological approaches (as per Baylor et al. 2023; Bjerva 2024) for extending comparisons of discrete linguistic categories, and suggests that important insights can be gained even from noisy data, given sufficient annotation. The dependency-annotated corpora are also made available for research and collaboration via GitHub.