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Subject
News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Amateur Prose On The Web: Verb Construction As A Feature Of Genre Classification

.
Builova N.

In our research we studied the dependency structure of the text genre love stories, detective stories, science fiction and fantasy). The novel characteristics (such syntactic attributes as verb constructions and construction of a specific cumulative threshold) which can be additional machine learning parameters were identified. We conducted experiment with novel features and showed that these characteristics can be useful for closely related genre recognition.

Language: English
Full text
Keywords: universal dependenciesmachine learning techniquesverb constractions
Publication based on the results of:
Материалы к частотному словарю русской поэзии (2018)

In book

Proceedings of Third Workshop "Computational linguistics and language science"
Wohlgenannt G., von Waldenfels R., Toldova S., Rakhilina E. V., Lyashevskaya O., Loukachevitch N. V., Artemova E. Issue 4. , Manchester: EasyChair, 2019.
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Added: May 31, 2024
Building a Universal Dependencies Treebank for a Polysynthetic Language: the Case of Abaza
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In this paper, we discuss the challenges that we faced during the construction of a Universal Dependencies treebank for Abaza, a polysynthetic Northwest Caucasian language. We propose an alternative to the morpheme-level annotation of polysynthetic languages introduced in Park et al. (2021). Our approach aims at reducing the number of morphological features, yet providing all ...
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Proceedings of the Sixth Workshop on Universal Dependencies (UDW, GURT/SyntaxFest 2023)
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An HMM-based PoS tagger for Old Church Slavonic
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2020 43rd International Conference on Telecommunications and Signal Processing (TSP)
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MorphoRuEval-2017: an Evaluation Track for the Automatic Morphological Analysis Methods for Russian
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Added: December 15, 2017
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Added: December 11, 2017
Mining Intelligence and Knowledge Exploration. 4th International Conference, MIKE 2016, Mexico City, Mexico, November 13 - 19, 2016, Revised Selected Papers
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Added: June 8, 2017
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