Extending dependencies to the taggedPBC: Word order in transitive clauses

2025-06-07

AI Summary

The taggedPBC dataset—a massive multilingual POS-tagged corpus covering over 1,500 languages—lacks dependency annotations, hindering cross-linguistic syntactic and typological research. Method: We propose the first dependency parsing transfer framework for taggedPBC, integrating cross-lingual POS alignment, dependency structure projection, and word-order statistical modeling. We further conduct empirical correlation analyses with authoritative typological databases (WALS, Grambank, Autotyp). Contribution/Results: Experiments demonstrate high agreement between automatically inferred argument–predicate word-order types in transitive clauses and expert annotations, validating corpus-based typological inference under noisy conditions. We publicly release the complete set of CoNLLU-formatted dependency annotations, establishing the first large-scale, broadly covered, and fully reproducible benchmark for cross-linguistic dependency syntax and linguistic typology research.

Technology Category

• Natural Language Processing: Syntax — Tagging, Chunking & Parsing
• Knowledge Representation and Reasoning: Argumentation
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

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.

Problem

• Extends dependency annotation to taggedPBC dataset
• Validates word order in transitive clauses crosslinguistically
• Assesses noisy data utility for typological insights

Innovation

• Extends taggedPBC with dependency annotations
• Uses CoNLLU format for crosslinguistic data
• Validates word order via typological databases