The taggedPBC: Annotating a massive parallel corpus for crosslinguistic investigations

TL;DR

The paper introduces the taggedPBC, a large parallel corpus with POS tags from over 1,940 languages, enabling crosslinguistic research and demonstrating its utility through correlation with typological features.

Contribution

It presents a massive, multilingual tagged corpus that surpasses previous resources and introduces a novel typological measure derived from it for linguistic analysis.

Findings

High correlation of tag accuracy with existing taggers and hand-tagged corpora.

The N1 ratio correlates with expert typological classifications.

A classifier trained on N1 can predict intransitive word order in unseen languages.

Abstract

Existing datasets available for crosslinguistic investigations have tended to focus on large amounts of data for a small group of languages or a small amount of data for a large number of languages. This means that claims based on these datasets are limited in what they reveal about universal properties of the human language faculty. While this has begun to change through the efforts of projects seeking to develop tagged corpora for a large number of languages, such efforts are still constrained by limits on resources. The current paper reports on a large tagged parallel dataset which has been developed to partially address this issue. The taggedPBC contains POS-tagged parallel text data from more than 1,940 languages, representing 155 language families and 78 isolates, dwarfing previously available resources. The accuracy of particular tags in this dataset is shown to correlate well with both existing SOTA taggers for high-resource languages (SpaCy, Trankit) as well as hand-tagged corpora (Universal Dependencies Treebanks). Additionally, a novel measure derived from this dataset, the N1 ratio, correlates with expert determinations of intransitive word order in three typological databases (WALS, Grambank, AUTOYP) such that a Gaussian Naive Bayes classifier trained on this feature can accurately identify basic intransitive word order for languages not in those databases. While much work is still needed to expand and develop this dataset, the taggedPBC is an important step to enable corpus-based crosslinguistic investigations, and is made available for research and collaboration via GitHub.