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# Iryna Gurevych

7 paper cards in Mova's library · 2010–2023

Iryna Gurevych wrote 7 of the papers Mova reads, in 2010–2023, most of them on [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/), [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/), [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/); often with [Bonnie Webber](https://md.mova.elamur.ai/free/en/docs/papers/authors/bonnie-webber/), Christian M. Meyer, [Delphine Bernhard](https://md.mova.elamur.ai/free/en/docs/papers/authors/delphine-bernhard/).

[OpenAlex](https://openalex.org/A5027450194) · [ORCID](https://orcid.org/0000-0003-2187-7621) · [**h-index 73**](https://openalex.org/authors/A5027450194) · [34089 citations](https://openalex.org/authors/A5027450194) · [861 works](https://openalex.org/works?filter=author.id:A5027450194) — 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=Iryna+Gurevych) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Iryna+Gurevych) · [dblp](https://dblp.org/search?q=Iryna+Gurevych) · [ACL Anthology](https://aclanthology.org/search/?q=Iryna+Gurevych) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Iryna+Gurevych)

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 2 · [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) 1 · [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/) 1

## Papers

1. **[Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future](https://md.mova.elamur.ai/free/en/docs/papers/cl-2023-klie-annotation-error-detection-analyzing-past/)** *(2023)* The authors survey methods for finding annotation errors, reimplement 18 of them, and test them on 9 English datasets for text classification, token labelling and span labelling.
2. **[IMPLI: Investigating NLI Models' Performance on Figurative Language](https://md.mova.elamur.ai/free/en/docs/papers/stowe-2022-impli-investigating-nli-models-performance/)** *(2022)* IMPLI pairs sentences containing an idiom or metaphor with a literal sentence that is or is not entailed by it: 24,029 silver pairs and 1,831 hand-written gold pairs.
3. **[BinLin: A Simple Method of Dependency Tree Linearization](https://md.mova.elamur.ai/free/en/docs/papers/puzikov-2018-binlin-simple-method-dependency-tree/)** *(2018)* The paper is a system report for the shallow track of SR'18. The input is a bag of lemmatised UD nodes with their tags and morphology. The system works in two stages: it first orders the lemmas, and then it conjugates and inflects each word. The contribution is a sequential way of ordering lemmas. It is simple and it…
4. **[Out-of-domain FrameNet Semantic Role Labeling](https://md.mova.elamur.ai/free/en/docs/papers/hartmann-2017-out-domain-framenet-semantic-role/)** *(2017)* FrameNet role labeling is usually tested on one in-domain set (das-test). The authors test how it generalises. They build YAGS, a new test set of 1,415 sentences from Yahoo! Answers with 3,091 frame and 6,081 role annotations. Inter-rater agreement is Krippendorff's alpha 0.76 for frames and 0.62 for roles given…
5. **[WordNet—Wikipedia—Wiktionary: Construction of a Three-way Alignment](https://md.mova.elamur.ai/free/en/docs/papers/lex-2014-miller-wordnet-wikipedia-wiktionary/)** *(2014)* The paper builds a three-way alignment of senses from existing pairwise alignments of WordNet with Wikipedia and with Wiktionary.
6. **[To Exhibit is not to Loiter: A Multilingual, Sense-Disambiguated Wiktionary for Measuring Verb Similarity](https://md.mova.elamur.ai/free/en/docs/papers/lex-2012-meyer-sense-disambiguated-wiktionary/)** *(2012)* The paper turns Wiktionary's semantic relations and translations into a sense-disambiguated resource. Each relation or translation is linked to one sense of its target word by a method that combines features such as a monolingual-only candidate, a source-overlap score, a lexical-expansion score, a relation score and…
7. **[A Monolingual Tree-based Translation Model for Sentence Simplification](https://md.mova.elamur.ai/free/en/docs/papers/zhu-2010-monolingual-tree-based-translation-model/)** *(2010)* The paper treats sentence simplification as translation from complex to simple sentences, with the complex parse tree as the source.

## Co-authors (12)

- [Bonnie Webber](https://md.mova.elamur.ai/free/en/docs/papers/authors/bonnie-webber/)
- Christian M. Meyer
- [Delphine Bernhard](https://md.mova.elamur.ai/free/en/docs/papers/authors/delphine-bernhard/)
- Ilia Kuznetsov
- Jan-Christoph Klie
- [Kevin Stowe](https://md.mova.elamur.ai/free/en/docs/papers/authors/kevin-stowe/)
- Prasetya Utama
- Silvana Hartmann
- Teresa Martin
- Tristan Miller
- Yevgeniy Puzikov
- Zhemin Zhu
- [← previous Irene Miani](https://md.mova.elamur.ai/free/en/docs/papers/authors/irene-miani/)
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