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Pattern Structures for Knowledge Processing and Information Retrieval
P. 410–420.
Kuznetsov S., Goncharova E.
Publication based on the results of:
In book
Vol. 330. , Springer, 2022.
Shumen: INCOMA Ltd, 2025.
This paper introduces a rule-based lemmatization and word embedding pipeline for the endangered Bartangi language, part of the Pamiri language group. The system combines a manually constructed lemma dictionary with morphological suffix rules to improve linguistic consistency in low-resource settings. The results demonstrate enhanced lemmatization accuracy and higher-quality embeddings for downstream NLP tasks. The work ...
Added: October 20, 2025
Skorinkin D., Orekhov B., , in: The Oxford Handbook of Global Realisms.: Oxford: Oxford University Press, 2025. Ch. 10 P. 177–204.
This chapter investigates literary prose of the realist era in Russia using digital humanities methods. It focuses on how computational analysis can enhance an understanding of descriptions of literary characters, geographical locations, and lexical composition in literary texts. Using a corpus of more than five hundred texts (forty-six million word occurrences), it eschews the focus ...
Added: September 14, 2025
Association for Computing Machinery (ACM), 2024.
Welcome to the 47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024), taking place in Washington D.C., USA, from July 14 to 18, 2024.
SIGIR serves as the foremost international forum for the presentation of groundbreaking research findings, the demonstration of innovative systems and techniques, and the exploration of forwardthinking ...
Added: May 9, 2024
Sergei Koltcov, Surkov A., Filippov V. et al., PeerJ Computer Science 2024 Vol. 10 P. 41
Topic modeling is a widely used instrument for the analysis of large text collections.
In the last few years, neural topic models and models with word embeddings have
been proposed to increase the quality of topic solutions. However, these models
were not extensively tested in terms of stability and interpretability. Moreover, the
question of selecting the number of topics ...
Added: February 16, 2024
Sergei O. Kuznetsov, Parakal E. G., Lecture Notes in Networks and Systems 2023 Vol. 776 P. 423–434
Inherently explainable Machine Learning (ML) models are able to provide explanations for their predictions by virtue of their construction. The explanations of a ML model are more comprehensible if they are expressed in terms of its input features. Our paper proposes an inherently explainable pipeline for document classification using pattern structures and Abstract Meaning Representation ...
Added: February 5, 2024
Springer, 2023.
This book constitutes the extended and revised versions of a set of selected papers from the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2021, on October 25–27, 2021. The conference was held virtually due to the COVID-19 crisis.
The 9 full papers included in this book were carefully reviewed and ...
Added: July 8, 2023
Springer, 2023.
Added: March 22, 2023
Ilya Semenkov, Sergei O. Kuznetsov, , in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)Vol. 2972.: CEUR-WS, 2021. P. 105–112.
This paper presents different versions of classification ensemble methods based on pattern structures. Each of these methods is described and tested on multiple datasets (including datasets with exclusively numerical and exclusively nominal features). As a baseline model Random Forest generation is used. For some classification tasks the classification algorithms based on pattern structures showed better ...
Added: December 19, 2022
Association for Computing Machinery (ACM), 2022.
Added: July 8, 2022
Sosnin A., Balakina Y. V., Кащихин А. Н., Вестник Санкт-Петербургского университета. Язык и литература 2022 Т. 19 № 1 С. 125–148
The article evaluates the quality of translation; we consider the applied and pragmatic aspects
of such evaluation in the conditions of the current rapid increase in the number of texts to be
translated. The article summarizes a plethora of assessment principles, each having its merits
and drawbacks, and examines the correlation between the categories of adequacy and equivalence
as ...
Added: May 31, 2022
Samenko I., Tikhonov A., Yamshchikov I. P., , in: Modern Management based on Big Data II and Machine Learning and Intelligent Systems IIIVol. 341.: IOS Press Ebooks, 2021. P. 502–510.
This paper shows that modern word embeddings contain information that distinguishes synonyms and antonyms despite small cosine similarities between corresponding vectors. This information is implicitly encoded in the geometry of the embeddings and could be extracted with a straightforward manifold learning procedure or a contrasting map. Such a map is trained on a small labeled ...
Added: December 2, 2021
Chistova E., Shelmanov A., Pisarevskaya D. et al., , in: Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected PapersVol. 12602.: Springer, 2021. P. 105–119.
This work presents the first fully-fledged discourse parser for
Russian based on the Rhetorical Structure Theory of Mann and Thompson
(1988). For the segmentation, discourse tree construction, and discourse
relation classification we employ deep learning models. With the
help of multiple word embedding techniques, the new state of the art
for discourse segmentation of Russian texts is achieved. We found ...
Added: November 17, 2021
Goncharova E., Ilvovsky D., Galitsky B., , in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)Vol. 2972.: CEUR-WS, 2021. P. 51–58.
Added: October 28, 2021
Bogomolov E., Golubev Y., Lobanov A. et al., , in: ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering.: ACM, 2020. P. 1316–1320.
Added: October 26, 2021