Olga Fedorova


2020

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Using the RUPEX Multichannel Corpus in a Pilot fMRI Study on Speech Disfluencies
Katerina Smirnova | Nikolay Korotaev | Yana Panikratova | Irina Lebedeva | Ekaterina Pechenkova | Olga Fedorova
Proceedings of the 12th Language Resources and Evaluation Conference

In modern linguistics and psycholinguistics speech disfluencies in real fluent speech are a well-known phenomenon. But it’s not still clear which components of brain systems are involved into its comprehension in a listener’s brain. In this paper we provide a pilot neuroimaging study of the possible neural correlates of speech disfluencies perception, using a combination of the corpus and functional magnetic-resonance imaging (fMRI) methods. Special technical procedure of selecting stimulus material from Russian multichannel corpus RUPEX allowed to create fragments in terms of requirements for the fMRI BOLD temporal resolution. They contain isolated speech disfluencies and their clusters. Also, we used the referential task for participants fMRI scanning. As a result, it was demonstrated that annotated multichannel corpora like RUPEX can be an important resource for experimental research in interdisciplinary fields. Thus, different aspects of communication can be explored through the prism of brain activation.

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CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles
Zubair Afzal | Vikrant Yadav | Olga Fedorova | Vaishnavi Kandala | Janneke van de Loo | Saber A. Akhondi | Pascal Coupet | George Tsatsaronis
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

Ever since the COVID-19 pandemic broke out, the academic and scientific research community, as well as industry and governments around the world have joined forces in an unprecedented manner to fight the threat. Clinicians, biologists, chemists, bioinformaticians, nurses, data scientists, and all of the affiliated relevant disciplines have been mobilized to help discover efficient treatments for the infected population, as well as a vaccine solution to prevent further the virus spread. In this combat against the virus responsible for the pandemic, key for any advancements is the timely, accurate, peer-reviewed, and efficient communication of any novel research findings. In this paper we present a novel framework to address the information need of filtering efficiently the scientific bibliography for relevant literature around COVID-19. The contributions of the paper are summarized in the following: we define and describe the information need that encompasses the major requirements for COVID-19 articles relevancy, we present and release an expert-curated benchmark set for the task, and we analyze the performance of several state-of-the-art machine learning classifiers that may distinguish the relevant from the non-relevant COVID-19 literature.

2018

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A «Portrait» Approach to Multichannel Discourse
Andrej Kibrik | Olga Fedorova
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2010

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Experimental Approach to Reference in Discourse: Working Memory Capacity and Language Comprehension in Russian
Olga Fedorova | Ekaterina Delikishkina | Anna Uspenskaya
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

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Why the English Easiest Type Became the Hardest in Russian, or Russian Adults’ Comprehension of before and after sentences
Olga Fedorova
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation