Maria Ryskina she/they

selected publications

1. ACL

   Phonetic and Visual Priors for Decipherment of Informal Romanization

   Maria Ryskina, Matthew R. Gormley, and Taylor Berg-Kirkpatrick

   In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 2020

   Abstract arXiv Bib Code/Data Slides Talk

   Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards. Character substitution choices differ between users but have been shown to be governed by the same main principles observed across a variety of languages—namely, character pairs are often associated through phonetic or visual similarity. We propose a noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion. We train our model directly on romanized data from two languages: Egyptian Arabic and Russian. We demonstrate that adding inductive bias through phonetic and visual priors on character mappings substantially improves the model’s performance on both languages, yielding results much closer to the supervised skyline. Finally, we introduce a new dataset of romanized Russian, collected from a Russian social network website and partially annotated for our experiments.

   @inproceedings{ryskina-etal-2020-phonetic,
     title = "Phonetic and Visual Priors for Decipherment of Informal Romanization",
     author = "Ryskina, Maria  and
       Gormley, Matthew R.  and
       Berg-Kirkpatrick, Taylor",
     booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.acl-main.737",
     doi = "10.18653/v1/2020.acl-main.737",
     pages = "8308–8319",
   }

2. SCiL

   Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods

   Maria Ryskina, Ella Rabinovich, Taylor Berg-Kirkpatrick, David R. Mortensen, and Yulia Tsvetkov

   In Proceedings of the Society for Computation in Linguistics 2020

   Abstract arXiv Bib Code Poster

   We perform statistical analysis of the phenomenon of neology, the process by which new words emerge in a language, using large diachronic corpora of English. We investigate the importance of two factors, semantic sparsity and frequency growth rates of semantic neighbors, formalized in the distributional semantics paradigm. We show that both factors are predictive of word emergence although we find more support for the latter hypothesis. Besides presenting a new linguistic application of distributional semantics, this study tackles the linguistic question of the role of language-internal factors (in our case, sparsity) in language change motivated by language-external factors (reflected in frequency growth).

   @inproceedings{ryskina-etal-2020-new,
     title = "Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods",
     author = "Ryskina, Maria  and
       Rabinovich, Ella  and
       Berg-Kirkpatrick, Taylor  and
       Mortensen, David  and
       Tsvetkov, Yulia",
     booktitle = "Proceedings of the Society for Computation in Linguistics",
     volume = "3",
     number = "1",
     month = jan,
     year = "2020",
     address = "New Orleans, Louisiana",
     url = "https://scholarworks.umass.edu/scil/vol3/iss1/6/",
     doi = "10.7275/1jra-8m83",
     pages = "43–52",
   }

3. ACL

   Automatic Compositor Attribution in the First Folio of Shakespeare

   Maria Ryskina, Hannah Alpert-Abrams, Dan Garrette, and Taylor Berg-Kirkpatrick

   In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics 2017

   Abstract arXiv Bib Poster

   Compositor attribution, the clustering of pages in a historical printed document by the individual who set the type, is a bibliographic task that relies on analysis of orthographic variation and inspection of visual details of the printed page. In this paper, we introduce a novel unsupervised model that jointly describes the textual and visual features needed to distinguish compositors. Applied to images of Shakespeare’s First Folio, our model predicts attributions that agree with the manual judgements of bibliographers with an accuracy of 87%, even on text that is the output of OCR.

   @inproceedings{ryskina-etal-2017-automatic,
     title = "Automatic Compositor Attribution in the {F}irst {F}olio of {S}hakespeare",
     author = "Ryskina, Maria  and
       Alpert-Abrams, Hannah  and
       Garrette, Dan  and
       Berg-Kirkpatrick, Taylor",
     booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
     month = jul,
     year = "2017",
     address = "Vancouver, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/P17-2065",
     doi = "10.18653/v1/P17-2065",
     pages = "411–416",
   }