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Modeling Global Syntactic Variation in English Using Dialect Classification

What it is about

The paper asks which English national varieties exist, and then models their syntax. Geo-referenced web text (Common Crawl, 16.65 billion words) and tweets (4.14 billion words) are used; a variety is kept if it has at least 15 million words in both sources, which gives 14 national varieties. The features are constructions induced from a background corpus (a construction grammar, CxG-2), and a linear SVM classifies 1,000-word samples. Within-domain F1 is 0.96 on web text and 0.92 on Twitter, above function words (0.65 and 0.55) and the older grammar (0.80 and 0.76), and below unigrams (1.00 and 1.00). Across domains the F1 falls to 0.33 (trained on web, tested on Twitter) and 0.40 (the reverse). The paper also runs unmasking: removing the most predictive features round by round, the syntactic model degrades faster than the lexical one.

Ideas

For Mova

Chapters: 37. Register and variation (the cross-register measure), 08. National varieties (the data-driven choice of varieties), 32. Politeness and hedging (hedge patterns for idea I2). Code: prag (hedges and polarity), en/src/tag.rs and en/src/parse.rs for the UD features that a pattern would use. Measures: F1 within register and across registers; the unmasking curve; the BTreeMap-ordered pattern counts must give identical output on two runs.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/w19-1405

@inproceedings{dunn2019modeling,
  title = {{Modeling Global Syntactic Variation in English Using Dialect Classification}},
  author = {Dunn, Jonathan},
  booktitle = {Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects},
  year = {2019},
  doi = {10.18653/v1/w19-1405},
  url = {https://doi.org/10.18653/v1/w19-1405}
}

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