Ruslan Mitkov

Computational Phraseology light: automatic translation of multiword expressions without translation resources

2016 · Yearbook of Phraseology

Abstract This paper describes the first phase of a project whose ultimate goal is the implementation of a practical tool to support the work of language learners and translators by automatically identifying multiword expressions (MWEs) …

WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony

2018

This paper describes the systems submitted to SemEval 2018 Task 3 "Irony detection in English tweets" for both subtasks A and B. The first system leveraging a combination of sentiment, distributional semantic, and text surface …

RGCL at GermEval 2019: Offensive Language Detection with Deep Learning.

2019 · Open Repository and Bibliography (University of Luxembourg)

This paper describes the system submitted by the RGCL team to GermEval 2019 Shared Task 2: Identification of Offensive
\nLanguage. We experimented with five different neural network architectures in order to classify Tweets in terms of …

Fiction in Russian Translation: A Translationese Study

2021

This paper presents a translationese study based on the parallel data from the Russian National Corpus (RNC). We explored differences between literary texts originally authored in Russian and fiction translated into Russian from 11 languages. …

Paragraph Similarity Matches for Generating Multiple-choice Test Items

2021 · Student Research Workshop .../Proceedings of the Student Research Workshop ...

Multiple-choice questions (MCQs) are widely used in knowledge assessment in educational institutions, during work interviews, in entertainment quizzes and games. Although the research on the automatic or semi-automatic generation of multiple-choice test items has been …

From Text to Graph: Leveraging Graph Neural Networks for Enhanced Explainability in NLP

2025 · arXiv (Cornell University)

Researchers have relegated natural language processing tasks to Transformer-type models, particularly generative models, because these models exhibit high versatility when performing generation and classification tasks. As the size of these models increases, they achieve outstanding …