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# Modeling Global Syntactic Variation in English Using Dialect Classification

**Authors:** [Jonathan Dunn](https://md.mova.elamur.ai/free/en/docs/papers/authors/jonathan-dunn/) · **Year:** 2019 · **Venue:** Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2019), pp. 42–53 **Link:** [aclanthology.org/W19-1405](https://aclanthology.org/W19-1405/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**11** citations](https://openalex.org/works?filter=cites:W2963764322) openalex · 2026-10-19[**12** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dunn-2019-modeling-global-syntactic-variation-english/) of the 140673 papers whose bibliographies we read[Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects](https://aclanthology.org/volumes/W19-14/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/W19-1405/)
- [PDF](https://aclanthology.org/W19-1405.pdf)
- [DOI 10.18653/v1/w19-1405](https://doi.org/10.18653/v1/w19-1405)
- [OpenAlex](https://openalex.org/W2963764322)

**Authors (1)**

1. [Dunn, Jonathan](https://openalex.org/A5063420865) [ORCID](https://orcid.org/0000-0002-1189-1908) · [Semantic Scholar](https://www.semanticscholar.org/author/49395862) · h 13 · 562 citations · 68 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## 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

- I1. Report a cross-register drop for any rule or pattern model: train the `en` tagger or the pattern counts on one register, test on another, and print both numbers next to each other. A large drop tells the operator the model learned register, not structure.
- I2. Unmasking as a check on the explanation: in `prag`, remove the top-k cues of a hedge or polarity classifier in rounds and record the accuracy curve. A rule set that collapses after a few removals depends on a few words.
- I3. Feature density per sample as a coverage measure: count, for each 1,000-word sample, how many learned patterns fire. A low count for a variety means the grammar misses its constructions, which is a measurable gap and not a wrong answer.

## For Mova

Chapters: [37. Register and variation](https://md.mova.elamur.ai/free/en/docs/languages/en/37-register-and-variation/) (the cross-register measure), [08. National varieties](https://md.mova.elamur.ai/free/en/docs/languages/en/08-national-varieties/) (the data-driven choice of varieties), [32. Politeness and hedging](https://md.mova.elamur.ai/free/en/docs/languages/en/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](https://doi.org/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}
}
```

- [← previous Linguistic Features in Automatic Sarcasm Detection](https://md.mova.elamur.ai/free/en/docs/papers/ducret-2020-linguistic-features-automatic-sarcasm-detection/)
- [next → Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGs](https://md.mova.elamur.ai/free/en/docs/papers/dunn-2021-finding-variants-construction-based-dialectometry/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

57 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Donoso, G. and Sanchez, D. 2017. Dialectometric analysis of language variation in twitter. In Proceedings of the 4th Workshop on NLP for Similar Languages, Varieties and Dialects. 16-25. [find](https://scholar.google.com/scholar?q=Donoso%2C+G%2E+and+Sanchez%2C+D%2E+2017%2E+Dialectometric+analysis+of+language+variation+in+twitter%2E+In+Proceedings+of+the+4th+Workshop+on+NLP+for+Similar+Languages%2C+Varieties+and+Dialects%2E+16%2D25%2E)
2. Adams, B. 2015. Finding Similar Places using the Observation-to-Generalization Place Model. Journal of Geographical Systems, 17(2): 137-156. [find](https://scholar.google.com/scholar?q=Adams%2C+B%2E+2015%2E+Finding+Similar+Places+using+the+Observation%2Dto%2DGeneralization+Place+Model%2E+Journal+of+Geographical+Systems%2C+17%282%29%3A+137%2D156%2E)
3. Dunn, J. 2017. Computational Learning of Construction Grammars. Language & Cognition, 9(2): 254-292. [find](https://scholar.google.com/scholar?q=Dunn%2C+J%2E+2017%2E+Computational+Learning+of+Construction+Grammars%2E+Language+%26+Cognition%2C+9%282%29%3A+254%2D292%2E)
4. Adams, B. and McKenzie, G. 2018. Crowdsourcing the character of a place: Character-level convolutional networks for multilingual geographic text classification. Transactions in GIS, doi: 10.1111/tgis.12317. [↗](https://doi.org/10.1111/tgis.12317)
5. Dunn, J. 2018a. Modeling the Complexity and Descriptive Adequacy of Construction Grammars. In Proceedings of the Society for Computation in Linguistics (SCiL 2018). Stroudsburg, PA: Association for Computational Linguistics. 81-90. [find](https://scholar.google.com/scholar?q=Dunn%2C+J%2E+2018a%2E+Modeling+the+Complexity+and+Descriptive+Adequacy+of+Construction+Grammars%2E+In+Proceedings+of+the+Society+for+Computation+in+Linguistics+%28SCiL+2018%29%2E+Stroudsburg%2C+PA%3A+Association+for+Co)
6. Ali, M. 2018. Character Level Convolutional Neural Network for Arabic Dialect Identification. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects, 122-127. [find](https://scholar.google.com/scholar?q=Ali%2C+M%2E+2018%2E+Character+Level+Convolutional+Neural+Network+for+Arabic+Dialect+Identification%2E+In+Proceedings+of+the+Fifth+Workshop+on+NLP+for+Similar+Languages%2C+Varieties+and+Dialects%2C+122%2D127%2E)
7. Dunn, J. 2018b. Multi-Unit Directional Measures of Association: Moving Beyond Pairs of Words. International Journal of Corpus Linguistics, 23(2): 183-215. [find](https://scholar.google.com/scholar?q=Dunn%2C+J%2E+2018b%2E+Multi%2DUnit+Directional+Measures+of+Association%3A+Moving+Beyond+Pairs+of+Words%2E+International+Journal+of+Corpus+Linguistics%2C+23%282%29%3A+183%2D215%2E)
8. Argamon, S. and Koppel, M. 2013. A Systemic Functional Approach to Automated Authorship Analysis. Journal of Law & Policy, 12:299-315. [find](https://scholar.google.com/scholar?q=Argamon%2C+S%2E+and+Koppel%2C+M%2E+2013%2E+A+Systemic+Functional+Approach+to+Automated+Authorship+Analysis%2E+Journal+of+Law+%26+Policy%2C+12%3A299%2D315%2E)
9. Dunn, J. 2018c. Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGs. Cognitive Linguistics, 29(2): 275-311. [find](https://scholar.google.com/scholar?q=Dunn%2C+J%2E+2018c%2E+Finding+Variants+for+Construction%2DBased+Dialectometry%3A+A+Corpus%2DBased+Approach+to+Regional+CxGs%2E+Cognitive+Linguistics%2C+29%282%29%3A+275%2D311%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/dunn-2021-finding-variants-construction-based-dialectometry/)
10. Barbaresi, A. 2018. Computationally efficient discrimination between language varieties with large feature vectors and regularized classifiers. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects, 164-171. [find](https://scholar.google.com/scholar?q=Barbaresi%2C+A%2E+2018%2E+Computationally+efficient+discrimination+between+language+varieties+with+large+feature+vectors+and+regularized+classifiers%2E+In+Proceedings+of+the+Fifth+Workshop+on+NLP+for+Similar+)
11. Dunn, J. 2019. Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Dunn%2C+J%2E+2019%2E+Frequency+vs%2E+Association+for+Constraint+Selection+in+Usage%2DBased+Construction+Grammar%2E+In+Proceedings+of+the+Workshop+on+Cognitive+Modeling+and+Computational+Linguistics%2E+Association+f)
12. Baroni, M., Bernardini, S., Ferraresi, A., and Zanchetta, E. 2009. The WaCky Wide Web: A Collection of Very Large Linguistically Processed Web-crawled Corpora. Language Resources and Evaluation, 43: 209-226. [find](https://scholar.google.com/scholar?q=Baroni%2C+M%2E%2C+Bernardini%2C+S%2E%2C+Ferraresi%2C+A%2E%2C+and+Zanchetta%2C+E%2E+2009%2E+The+WaCky+Wide+Web%3A+A+Collection+of+Very+Large+Linguistically+Processed+Web%2Dcrawled+Corpora%2E+Language+Resources+and+Evaluation%2C+43%3A+2)
13. Dunn, J.; Argamon, S.; Rasooli, A.; and Kumar, G. 2016. Profile-based authorship analysis. Literary and Linguistic Computing, 31(4): 689-710. [find](https://scholar.google.com/scholar?q=Dunn%2C+J%2E%3B+Argamon%2C+S%2E%3B+Rasooli%2C+A%2E%3B+and+Kumar%2C+G%2E+2016%2E+Profile%2Dbased+authorship+analysis%2E+Literary+and+Linguistic+Computing%2C+31%284%29%3A+689%2D710%2E)
14. Belinkov, Y. and Glass, J. 2016. A Character-level Convolutional Neural Network for Distinguishing Similar Languages and Dialects. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects, 145-152. [find](https://scholar.google.com/scholar?q=Belinkov%2C+Y%2E+and+Glass%2C+J%2E+2016%2E+A+Character%2Dlevel+Convolutional+Neural+Network+for+Distinguishing+Similar+Languages+and+Dialects%2E+In+Proceedings+of+the+Third+Workshop+on+NLP+for+Similar+Languages%2C+Va)
15. Eisenstein, J.; O’Connor, B.; Smith, N.; and Xing, E. 2010. A latent variable model for geographic lexical variation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 1,227-1,287. [find](https://scholar.google.com/scholar?q=Eisenstein%2C+J%2E%3B+O%E2%80%99Connor%2C+B%2E%3B+Smith%2C+N%2E%3B+and+Xing%2C+E%2E+2010%2E+A+latent+variable+model+for+geographic+lexical+variation%2E+In+Proceedings+of+the+Conference+on+Empirical+Methods+in+Natural+Language+Processi)
16. Benko, V. 2014. Aranea: Yet Another Family of (Comparable) Web Corpora. In Proceedings of Text, Speech and Dialogue. 17th International Conference. 257-264. [find](https://scholar.google.com/scholar?q=Benko%2C+V%2E+2014%2E+Aranea%3A+Yet+Another+Family+of+%28Comparable%29+Web+Corpora%2E+In+Proceedings+of+Text%2C+Speech+and+Dialogue%2E+17th+International+Conference%2E+257%2D264%2E)
17. Eisenstein, J.; O’Connor, B.; Smith, N.; and Xing, E. 2014. Diffusion of lexical change in social media. PloSOne, 10.1371. [find](https://scholar.google.com/scholar?q=Eisenstein%2C+J%2E%3B+O%E2%80%99Connor%2C+B%2E%3B+Smith%2C+N%2E%3B+and+Xing%2C+E%2E+2014%2E+Diffusion+of+lexical+change+in+social+media%2E+PloSOne%2C+10%2E1371%2E)
18. Calle-Martin, J. and Romero-Barranco, J. 2017. Third person present tense markers in some varieties of English. English World-Wide, 38(1): 77-103. [find](https://scholar.google.com/scholar?q=Calle%2DMartin%2C+J%2E+and+Romero%2DBarranco%2C+J%2E+2017%2E+Third+person+present+tense+markers+in+some+varieties+of+English%2E+English+World%2DWide%2C+38%281%29%3A+77%2D103%2E)
19. Gamallo, P.; Pichel, J.; Algeria, I.; and Agirrezabal, M. 2016. Comparing two Basic Methods for Discriminating Between Similar Languages and Varieties. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects, 170-177. [find](https://scholar.google.com/scholar?q=Gamallo%2C+P%2E%3B+Pichel%2C+J%2E%3B+Algeria%2C+I%2E%3B+and+Agirrezabal%2C+M%2E+2016%2E+Comparing+two+Basic+Methods+for+Discriminating+Between+Similar+Languages+and+Varieties%2E+In+Proceedings+of+the+Third+Workshop+on+NLP+for+)
20. Clark, L and Watson, K. 2016. Phonological leveling, diffusion, and divergence: /t/ lenition in Liverpool and its hinterland. Language Variation and Change, 28(1): 31-62. [find](https://scholar.google.com/scholar?q=Clark%2C+L+and+Watson%2C+K%2E+2016%2E+Phonological+leveling%2C+diffusion%2C+and+divergence%3A+%2Ft%2F+lenition+in+Liverpool+and+its+hinterland%2E+Language+Variation+and+Change%2C+28%281%29%3A+31%2D62%2E)
21. Ginter, F.; Hajiĉ, J.; Luotolahti, J. 2017. CoNLL 2017 Shared Task - Automatically Annotated Raw Texts and Word Embeddings, LIN-DAT/CLARIN digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University. [http://hdl.handle.net/11234/1-1989](http://hdl.handle.net/11234/1-1989). [↗](http://hdl.handle.net/11234/1-1989)
22. Collins, P. 2012. Singular agreement in there-existentials: An intervarietal corpus-based study. English World-Wide, 33(1): 53-68. [find](https://scholar.google.com/scholar?q=Collins%2C+P%2E+2012%2E+Singular+agreement+in+there%2Dexistentials%3A+An+intervarietal+corpus%2Dbased+study%2E+English+World%2DWide%2C+33%281%29%3A+53%2D68%2E)
23. Cook, P. and Brinton, J. 2017. Building and evaluating web corpora representing national varieties of English. Language Resources and Evaluation, 51. [find](https://scholar.google.com/scholar?q=Cook%2C+P%2E+and+Brinton%2C+J%2E+2017%2E+Building+and+evaluating+web+corpora+representing+national+varieties+of+English%2E+Language+Resources+and+Evaluation%2C+51%2E)
24. Goldberg, A. 2006. Constructions at Work: The Nature of Generalization in Language. Oxford: Oxford University Press. [find](https://scholar.google.com/scholar?q=Goldberg%2C+A%2E+2006%2E+Constructions+at+Work%3A+The+Nature+of+Generalization+in+Language%2E+Oxford%3A+Oxford+University+Press%2E)
25. Davies, M. and Fuchs, R. 2015. Expanding horizons in the study of World Englishes with the 1.9 billion word Global Web-based English Corpus (GloWbE). English World-Wide, 36(1): 1-28. [find](https://scholar.google.com/scholar?q=Davies%2C+M%2E+and+Fuchs%2C+R%2E+2015%2E+Expanding+horizons+in+the+study+of+World+Englishes+with+the+1%2E9+billion+word+Global+Web%2Dbased+English+Corpus+%28GloWbE%29%2E+English+World%2DWide%2C+36%281%29%3A+1%2D28%2E)
26. Goldhahn, d.; Eckart, T.; and Quasthoff, U. 2012. Building Large Monolingual Dictionaries at the Kreutz, T. and Daelemans, W. 2018. Exploring Classifier Combinations for Language Variety Identification. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects, 191-198. Leipzig Corpora Collection: From 100 to 200 Languages. In Proceedings of the Conference on Language Resources and Evaluation. [find](https://scholar.google.com/scholar?q=Goldhahn%2C+d%2E%3B+Eckart%2C+T%2E%3B+and+Quasthoff%2C+U%2E+2012%2E+Building+Large+Monolingual+Dictionaries+at+the+Kreutz%2C+T%2E+and+Daelemans%2C+W%2E+2018%2E+Exploring+Classifier+Combinations+for+Language+Variety+Identificatio)
27. Grafmiller, J. & Szmrecsanyi, B. 2018. Mapping out particle placement in Englishes around the world: A study in comparative sociolinguistic analysis. Language Variation and Change, 30(3): 385-412. [find](https://scholar.google.com/scholar?q=Grafmiller%2C+J%2E+%26+Szmrecsanyi%2C+B%2E+2018%2E+Mapping+out+particle+placement+in+Englishes+around+the+world%3A+A+study+in+comparative+sociolinguistic+analysis%2E+Language+Variation+and+Change%2C+30%283%29%3A+385%2D412%2E)
28. Kroon, M.; Medvedeva, M.; and Plank, B. 2018. When Simple n-gram Models Outperform Syntactic Approaches: Discriminating between Dutch and Flemish. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects, 244-25. [find](https://scholar.google.com/scholar?q=Kroon%2C+M%2E%3B+Medvedeva%2C+M%2E%3B+and+Plank%2C+B%2E+2018%2E+When+Simple+n%2Dgram+Models+Outperform+Syntactic+Approaches%3A+Discriminating+between+Dutch+and+Flemish%2E+In+Proceedings+of+the+Fifth+Workshop+on+NLP+for+Simil)
29. Graham, S.; Hale, S.; and Gaffney, D. 2014. Where in the world are you? Geolocation and language identification on twitter. The Professional Geographer, 66(4). [find](https://scholar.google.com/scholar?q=Graham%2C+S%2E%3B+Hale%2C+S%2E%3B+and+Gaffney%2C+D%2E+2014%2E+Where+in+the+world+are+you%3F+Geolocation+and+language+identification+on+twitter%2E+The+Professional+Geographer%2C+66%284%29%2E)
30. Kruger, H. and van Rooy, Bertus. 2018. Register variation in written contact varieties of English: A multidimensional analysis. English World-Wide, 39(2): 214-242. [find](https://scholar.google.com/scholar?q=Kruger%2C+H%2E+and+van+Rooy%2C+Bertus%2E+2018%2E+Register+variation+in+written+contact+varieties+of+English%3A+A+multidimensional+analysis%2E+English+World%2DWide%2C+39%282%29%3A+214%2D242%2E)
31. Grieve, J. 2007. Quantitative Authorship attribution: an evaluation of techniques. Literary and Linguistic Computing, 22(3): 251–70. [find](https://scholar.google.com/scholar?q=Grieve%2C+J%2E+2007%2E+Quantitative+Authorship+attribution%3A+an+evaluation+of+techniques%2E+Literary+and+Linguistic+Computing%2C+22%283%29%3A+251%E2%80%9370%2E)
32. Hirst, G. and Feiguina, O. 2007. Bigrams of syntactic labels for authorship discrimination of short texts. Literary and Linguistic Computing, 22(4): 405–17. [find](https://scholar.google.com/scholar?q=Hirst%2C+G%2E+and+Feiguina%2C+O%2E+2007%2E+Bigrams+of+syntactic+labels+for+authorship+discrimination+of+short+texts%2E+Literary+and+Linguistic+Computing%2C+22%284%29%3A+405%E2%80%9317%2E)
33. Labov, W.; Fisher, S.; Gylfadottir, D.; and Henderson, A. 2016. Competing systems in Philadelphia phonology. Language Variation and Change, 28(3): 273-305. [find](https://scholar.google.com/scholar?q=Labov%2C+W%2E%3B+Fisher%2C+S%2E%3B+Gylfadottir%2C+D%2E%3B+and+Henderson%2C+A%2E+2016%2E+Competing+systems+in+Philadelphia+phonology%2E+Language+Variation+and+Change%2C+28%283%29%3A+273%2D305%2E)
34. Hulden, M.; Silfverberg, M.; and Francom, J. 2015. Kernel Density Estimation for Text-Based Geolocation. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 145-150. [find](https://scholar.google.com/scholar?q=Hulden%2C+M%2E%3B+Silfverberg%2C+M%2E%3B+and+Francom%2C+J%2E+2015%2E+Kernel+Density+Estimation+for+Text%2DBased+Geolocation%2E+In+Proceedings+of+the+Twenty%2DNinth+AAAI+Conference+on+Artificial+Intelligence%2C+145%2D150%2E)
35. Langacker, R. 2008. Cognitive Grammar: A Basic Introduction. Oxford: Oxford University Press. [find](https://scholar.google.com/scholar?q=Langacker%2C+R%2E+2008%2E+Cognitive+Grammar%3A+A+Basic+Introduction%2E+Oxford%3A+Oxford+University+Press%2E)
36. Lourentzou, I.; Morales, A.; and Zhai, C. 2017. Text-based geolocation prediction of social media users with neural networks. In Proceedings of 2017 IEEE International Conference on Big Data, 696-705. [find](https://scholar.google.com/scholar?q=Lourentzou%2C+I%2E%3B+Morales%2C+A%2E%3B+and+Zhai%2C+C%2E+2017%2E+Text%2Dbased+geolocation+prediction+of+social+media+users+with+neural+networks%2E+In+Proceedings+of+2017+IEEE+International+Conference+on+Big+Data%2C+696%2D705%2E)
37. Ionescu, R. and Popescu, M. 2016. UnibucKernel: An Approach for Arabic Dialect Identification based on Multiple String Kernels. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects, 135-144. [find](https://scholar.google.com/scholar?q=Ionescu%2C+R%2E+and+Popescu%2C+M%2E+2016%2E+UnibucKernel%3A+An+Approach+for+Arabic+Dialect+Identification+based+on+Multiple+String+Kernels%2E+In+Proceedings+of+the+Third+Workshop+on+NLP+for+Similar+Languages%2C+Varie)
38. McNamee, P. 2016. Language and Dialect Discrimination Using Compression-Inspired Language Models. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects, 195-203. [find](https://scholar.google.com/scholar?q=McNamee%2C+P%2E+2016%2E+Language+and+Dialect+Discrimination+Using+Compression%2DInspired+Language+Models%2E+In+Proceedings+of+the+Third+Workshop+on+NLP+for+Similar+Languages%2C+Varieties+and+Dialects%2C+195%2D203%2E)
39. Joachims, T. 1998. Text categorization with support vector machines: Learning with many relevant features. In C. Nedellec (ed.), Machine learning: ECML-98: 10th European Conference on Machine Learning, 137-142. Berlin: Springer. [find](https://scholar.google.com/scholar?q=Joachims%2C+T%2E+1998%2E+Text+categorization+with+support+vector+machines%3A+Learning+with+many+relevant+features%2E+In+C%2E+Nedellec+%28ed%2E%29%2C+Machine+learning%3A+ECML%2D98%3A+10th+European+Conference+on+Machine+Learning)
40. Mocanu, D.; Baronchelli, A.; Perra, N.; Gonçalves, B.; Zhang, Q. and Vespignani, A. 2013. The Twitter of Babel: Mapping world languages through microblogging platforms. PLOSOne, 10.1371. [find](https://scholar.google.com/scholar?q=Mocanu%2C+D%2E%3B+Baronchelli%2C+A%2E%3B+Perra%2C+N%2E%3B+GonA%CC%83%C2%A7alves%2C+B%2E%3B+Zhang%2C+Q%2E+and+Vespignani%2C+A%2E+2013%2E+The+Twitter+of+Babel%3A+Mapping+world+languages+through+microblogging+platforms%2E+PLOSOne%2C+10%2E1371%2E)
41. Ju, Y.; Adams, B.; Janowicz, K.; Hu, Y.; Yan, B.; and McKenzie, G. 2016. Things and Strings: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence with Topic Modeling. In Proceedings of the 20th International Conference on Knowledge Engineering and Knowledge Management. LNCS, vol. 10024. Springer, pp. 353-367. [find](https://scholar.google.com/scholar?q=Ju%2C+Y%2E%3B+Adams%2C+B%2E%3B+Janowicz%2C+K%2E%3B+Hu%2C+Y%2E%3B+Yan%2C+B%2E%3B+and+McKenzie%2C+G%2E+2016%2E+Things+and+Strings%3A+Improving+Place+Name+Disambiguation+from+Short+Texts+by+Combining+Entity+Co%2DOccurrence+with+Topic+Modeling%2E)
42. Majli̧s, M. and Z̧abokrtský, Z. 2012. Language Richness of the Web. In Proceedings of the International Conference on Language Resources and Evaluation (LREC 2012). [https://ufal.mff.cuni.cz/w2c](https://ufal.mff.cuni.cz/w2c). [↗](https://ufal.mff.cuni.cz/w2c)
43. Kachru, B. 1990 The Alchemy of English: The spread, functions, and models of non-native Englishes. Urbana-Champaign: University of Illinois Press. [find](https://scholar.google.com/scholar?q=Kachru%2C+B%2E+1990+The+Alchemy+of+English%3A+The+spread%2C+functions%2C+and+models+of+non%2Dnative+Englishes%2E+Urbana%2DChampaign%3A+University+of+Illinois+Press%2E)
44. Rangel F., Rosso P., Potthast M., Stein B. 2017. Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter. In: Cappellato L., Ferro N., Goeuriot L, Mandl T. (eds.) CLEF 2017 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1866. [find](https://scholar.google.com/scholar?q=Rangel+F%2E%2C+Rosso+P%2E%2C+Potthast+M%2E%2C+Stein+B%2E+2017%2E+Overview+of+the+5th+Author+Profiling+Task+at+PAN+2017%3A+Gender+and+Language+Variety+Identification+in+Twitter%2E+In%3A+Cappellato+L%2E%2C+Ferro+N%2E%2C+Goeuriot+L%2C+)
45. Kondor, D.; Csabai, I.; Dobos, L.; Szule, J.; Barankai, N.; Hanyecz, T.; Sebok, T.; Kallus, Z.; and Vattay, G. 2013. Using robust PCA to estimate regional characteristics of language-use from geotagged twitter messages. In Proceedings of IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom). 393-398. [find](https://scholar.google.com/scholar?q=Kondor%2C+D%2E%3B+Csabai%2C+I%2E%3B+Dobos%2C+L%2E%3B+Szule%2C+J%2E%3B+Barankai%2C+N%2E%3B+Hanyecz%2C+T%2E%3B+Sebok%2C+T%2E%3B+Kallus%2C+Z%2E%3B+and+Vattay%2C+G%2E+2013%2E+Using+robust+PCA+to+estimate+regional+characteristics+of+language%2Duse+from+geotagge)
46. Roller, S.; Speriosu, M.; Rallapalli, S.; Wing, B.; and Baldridge, J. 2012. Supervised text-based geolocation using language models on an adaptive grid. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 1,500-1,510. [find](https://scholar.google.com/scholar?q=Roller%2C+S%2E%3B+Speriosu%2C+M%2E%3B+Rallapalli%2C+S%2E%3B+Wing%2C+B%2E%3B+and+Baldridge%2C+J%2E+2012%2E+Supervised+text%2Dbased+geolocation+using+language+models+on+an+adaptive+grid%2E+In+Proceedings+of+the+2012+Joint+Conference+on+)
47. Koppel, M., J. Schler, and E. Bonchek-Dokow. 2007. Measuring Differentiability: Unmasking Pseudonymous Authors. Journal of Machine Learning Research, 8: 1261-1276. [find](https://scholar.google.com/scholar?q=Koppel%2C+M%2E%2C+J%2E+Schler%2C+and+E%2E+Bonchek%2DDokow%2E+2007%2E+Measuring+Differentiability%3A+Unmasking+Pseudonymous+Authors%2E+Journal+of+Machine+Learning+Research%2C+8%3A+1261%2D1276%2E)
48. Sanders, N. 2007. Measuring syntactic difference in British English. In Proceedings of the 45th Annual Meeting of the ACL: Student Research Workshop,1-6. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Sanders%2C+N%2E+2007%2E+Measuring+syntactic+difference+in+British+English%2E+In+Proceedings+of+the+45th+Annual+Meeting+of+the+ACL%3A+Student+Research+Workshop%2C1%2D6%2E+Association+for+Computational+Linguistics%2E)
49. Sanders, N. 2010. A statistical method for syntactic dialectometry. Bloomington: Indiana. University dissertation. [find](https://scholar.google.com/scholar?q=Sanders%2C+N%2E+2010%2E+A+statistical+method+for+syntactic+dialectometry%2E+Bloomington%3A+Indiana%2E+University+dissertation%2E)
50. Schilk, M. and Schaub, S. 2016. Noun phrase complexity across varieties of English: Focus on syntactic function and text type. English World-Wide, 37(1): 58-85. [find](https://scholar.google.com/scholar?q=Schilk%2C+M%2E+and+Schaub%2C+S%2E+2016%2E+Noun+phrase+complexity+across+varieties+of+English%3A+Focus+on+syntactic+function+and+text+type%2E+English+World%2DWide%2C+37%281%29%3A+58%2D85%2E)
51. Schreier, D. 2016. Super-leveling, fraying-out, internal restructuring: A century of present be concord in Tristan da Cunha English. Language Variation and Change, 28(2): 203-224. [find](https://scholar.google.com/scholar?q=Schreier%2C+D%2E+2016%2E+Super%2Dleveling%2C+fraying%2Dout%2C+internal+restructuring%3A+A+century+of+present+be+concord+in+Tristan+da+Cunha+English%2E+Language+Variation+and+Change%2C+28%282%29%3A+203%2D224%2E)
52. Strelluf, C. 2016. Overlap among back vowels before /l/ in Kansas City. Language Variation and Change, 28(3): 379-407. [find](https://scholar.google.com/scholar?q=Strelluf%2C+C%2E+2016%2E+Overlap+among+back+vowels+before+%2Fl%2F+in+Kansas+City%2E+Language+Variation+and+Change%2C+28%283%29%3A+379%2D407%2E)
53. Szmrecsanyi, B.; Grafmiller, J.; Heller, B.; Rothlisberger, M. 2016. Around the world in three alternations: Modeling syntactic variation in varieties of English. English World-Wide, 37(2): 109-137. [find](https://scholar.google.com/scholar?q=Szmrecsanyi%2C+B%2E%3B+Grafmiller%2C+J%2E%3B+Heller%2C+B%2E%3B+Rothlisberger%2C+M%2E+2016%2E+Around+the+world+in+three+alternations%3A+Modeling+syntactic+variation+in+varieties+of+English%2E+English+World%2DWide%2C+37%282%29%3A+109%2D137%2E)
54. Tamaredo, I. 2018. Pronoun omission in high-contact varieties of English: Complexity versus efficiency. English World-Wide, 39(1): 85-110. [find](https://scholar.google.com/scholar?q=Tamaredo%2C+I%2E+2018%2E+Pronoun+omission+in+high%2Dcontact+varieties+of+English%3A+Complexity+versus+efficiency%2E+English+World%2DWide%2C+39%281%29%3A+85%2D110%2E)
55. Wing, B. and Baldridge, J. 2014. Hierarchical Discriminative Classification for Text-Based Geolocation. In Proceedings of the Conference on Empirical Methods in NLP, 336-348. [find](https://scholar.google.com/scholar?q=Wing%2C+B%2E+and+Baldridge%2C+J%2E+2014%2E+Hierarchical+Discriminative+Classification+for+Text%2DBased+Geolocation%2E+In+Proceedings+of+the+Conference+on+Empirical+Methods+in+NLP%2C+336%2D348%2E)
56. Zeman, D., et al. 2017. CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. In Proceedings of the Conference on Natural Language Learning, 1-19. [find](https://scholar.google.com/scholar?q=Zeman%2C+D%2E%2C+et+al%2E+2017%2E+CoNLL+2017+Shared+Task%3A+Multilingual+Parsing+from+Raw+Text+to+Universal+Dependencies%2E+In+Proceedings+of+the+Conference+on+Natural+Language+Learning%2C+1%2D19%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/ud-2017-zeman-conll-2017-shared-task-multilingual/)
57. Zeman, D., et al. 2018. CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. In Proceedings of the Conference on Natural Language Learning, 1-21. [find](https://scholar.google.com/scholar?q=Zeman%2C+D%2E%2C+et+al%2E+2018%2E+CoNLL+2018+Shared+Task%3A+Multilingual+Parsing+from+Raw+Text+to+Universal+Dependencies%2E+In+Proceedings+of+the+Conference+on+Natural+Language+Learning%2C+1%2D21%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/ud-2018-zeman-conll-2018-shared-task-multilingual/)