Автоматическая лингвистическая разметка китайских текстов, содержащих заимствования: словоделение, транскрипция, PoS-тэггинг

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September 11, 2026

How to Assess Students Knowledge in the Age of AI

A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.

September 9, 2026

‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction

You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.

September 9, 2026

Scientists Train Neural Network to Generate Process Plans from 3D Models

Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

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Автоматическая лингвистическая разметка китайских текстов, содержащих заимствования: словоделение, транскрипция, PoS-тэггинг

С. 1081–1094.

Konovalova A., Volf E., Семенов К. И., Korotkova Y.

The article tackles the problems of linguistic annotation of the Chinese texts presented in the Russian Chinese Parallel Corpus of RNC (hereafter – our corpus), and the ways to solve them. Particular attention is paid to the Rus - sian loanwords in the texts, as they, firstly, are abundant in our corpus, secondly, are of interest as the cases of both out-of-vocabulary and code-switching problems. We describe our experiments in three fields, namely, word segmentation, grapheme-to-phoneme conversion, and PoS-tagging. In order to test the algorithms on our specific data, we created our own datasets based on the corpus, which can be precious for the following research in the field of processing the non-standard Chinese texts. As the main aim of the research is to improve the quality of the annotation in our corpus, we plan to implement the results of our work in the preprocessing pipeline of the new texts in the corpus.

Language: Russian

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Keywords: chinese word segmentation grapheme-to-phoneme conversion (G2P)PoS-tagging out-of-vocabulary problem (OOV)code-switching detection автоматическая сегментация автоматическая транскрипция морфологическая аннотация проблема слов вне словаря автоматическое определение смены кодов

In book

Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)

Issue 20: Основной том. , -, 2021.