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# Cynthia Rudin

2 paper cards in Mova's library · 2008–2023

Cynthia Rudin wrote 2 of the papers Mova reads, in 2008–2023, most of them on [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/), [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/); often with Edwin Agnew, Lily Zhu, Michelle Qiu.

[OpenAlex](https://openalex.org/A5040468715) · [ORCID](https://orcid.org/0000-0003-4283-2780) · [**h-index 58**](https://openalex.org/authors/A5040468715) · [26033 citations](https://openalex.org/authors/A5040468715) · [492 works](https://openalex.org/works?filter=author.id:A5040468715) — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Cynthia+Rudin) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Cynthia+Rudin) · [dblp](https://dblp.org/search?q=Cynthia+Rudin) · [ACL Anthology](https://aclanthology.org/search/?q=Cynthia+Rudin) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Cynthia+Rudin)

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/) 1 · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 1

## Papers

1. **[The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation](https://md.mova.elamur.ai/free/en/docs/papers/agnew-2023-mechanical-bard-interpretable-machine-learning/)** *(2023)* The paper generates Shakespearean sonnets with a small pretrained model (GPT-2, fine-tuned on poems and sonnets) and enforces the sonnet form explicitly rather than hoping training teaches it.
2. **[Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2008-roth-arabic-morphological-tagging-diacritization-lemmatization/)** *(2008)* MADA picks, for each Arabic word, one analysis from the candidates that the BAMA dictionary proposes. It scores each candidate by summing the weights of the features on which the candidate agrees with the model's prediction. The features are 14 morphological tags, each predicted by an SVM, two checks (spelling match…

## Co-authors (8)

- Edwin Agnew
- Lily Zhu
- Michelle Qiu
- [Mona Diab](https://md.mova.elamur.ai/free/en/docs/papers/authors/mona-diab/)
- [Nizar Habash](https://md.mova.elamur.ai/free/en/docs/papers/authors/nizar-habash/)
- [Owen Rambow](https://md.mova.elamur.ai/free/en/docs/papers/authors/owen-rambow/)
- [Ryan Roth](https://md.mova.elamur.ai/free/en/docs/papers/authors/ryan-roth/)
- [Sam Wiseman](https://md.mova.elamur.ai/free/en/docs/papers/authors/sam-wiseman/)
- [← previous Cynthia L. Miller-Naudé](https://md.mova.elamur.ai/free/en/docs/papers/authors/cynthia-l-miller-naude/)
- [next → Cynthia Van Hee](https://md.mova.elamur.ai/free/en/docs/papers/authors/cynthia-van-hee/)
- [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)