?
Using Probability Distribution over Classes in Automatically Obtained Training Corpora
P. 90-93.
Durandin O., Hilal N., Strebkov D., Zolotykh N.
The paper contains a take on the classification problem variation featuring class noise where each object in the training set is associated with a probability distribution over the class label set instead of a particular class label. That type of task was illustrated on the complex natural language processing problem – automatic Arabic dialect classification. In the task we have a set of objects that were labeled by a heuristic rule; which could cause errors during automatic annotation process. Suggested approach allows taking into account probabilities of these errors. Described experiments show that even relatively simple accounting of that probabilities helps to significantly improve the quality of the built classifier.
Smetanin S., IEEE Access 2020 Vol. 8 P. 110693-110719
Sentiment analysis has become a powerful tool in processing and analysing expressed opinions on a large scale. While the application of sentiment analysis on English-language content has been widely examined, the applications on the Russian language remains not as well-studied. In this survey, we comprehensively reviewed the applications of sentiment analysis of Russian-language content and ...
Added: June 24, 2020
Bulychev A., Сомов О. Д., В кн. : Информатика, управление и системный анализ: Труды V Всероссийской научной конференции молодых ученых с международным участием. : Ростов н/Д : Ростовский государственный экономический университет "РИНХ", 2018. С. 94-102.
In the process of developing an information system for logistics transportation, there is a need to determine the initial rating of the new carrier within the parent company. The presence of the rating helps to more accurately carry out the formation of orders and build forecasts of its interaction with the parent company in the ...
Added: September 3, 2019
Springer, 2021
This book constitutes the proceedings of the 19th Russian Conference on Artificial Intelligence, RCAI 2021, held in Moscow, Russia, in October 2021.
The 19 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 80 submissions. The conference deals with a wide range of topics, categorized into the following topical ...
Added: October 28, 2021
Emmanuel I. C., Mitrofanova E., / Cornell Tech. Series 4064475 "ArXiv Preprint". 2022.
The paper is devoted to the study of the model fairness and process fairness of the Russian demographic dataset by making predictions of divorce of the 1st marriage, religiosity, 1st employment and completion of education. Our goal was to make classifiers more equitable by reducing their reliance on sensitive features while increasing or at least ...
Added: May 31, 2022
Vlasenko D., Заикин А. А., Zakharov D., Известия высших учебных заведений. Прикладная нелинейная динамика 2023 Т. 31 № 5 С. 661-669
Because the brain is an extremely complex hypernet of interacting macroscopic subnetworks, full-scale analysis of brain activity is a daunting task.Nevertheless,this task can be greatly simplified by analysing the correspondence between various patterns of macroscopic brain activity, forex ample,through functional magneticresonance imaging(fMRI) scans, and the performance of particular cognitive tasks or pathological states.The purpose of ...
Added: October 4, 2023
, in : 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP). : IEEE, 2017. P. 31-38.
In recent years many research works have been devoted either to anomaly detection or anomaly classification. However, very few of them address both of them simultaneously. In this paper, we introduced a new method not only to detect and localize the abnormalities in crowded scenes but also to determine the class of abnormality. In This ...
Added: November 1, 2020
Berlin : Springer, 2014
This book constitutes the proceedings of the Third International Conference on Analysis of Images, Social Networks and Texts, AIST 2014, held in Yekaterinburg, Russia, in April 2014. The 11 full and 10 short papers were carefully reviewed and selected from 74 submissions. They are presented together with 3 short industrial papers, 4 invited papers and ...
Added: November 13, 2014
I. K. Kusakin, Fedorets O. V., A. Y. Romanov, Scientific and Technical Information Processing 2023 Vol. 50 No. 3 P. 176-183
This paper discusses modern approaches to natural language processing and the application of machine learning models to the task of classifying short scientific texts in Russian. This study is devoted to the analysis of methods for vectorization of textual information, selection of a model for scientific paper clas- sification, and training of linguistic model BERT ...
Added: November 4, 2023
Springer, 2021
This book constitutes revised selected papers from the 9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020, held during October 15-16, 2020. The conference was planned to take place in Moscow, Russia, but changed to an online format due to the COVID-19 pandemic.
The 27 full papers and 4 short papers presented ...
Added: October 7, 2020
S.D. Kuznetsov, D.Yu. Turdakov, Астраханцев Н. А. et al., Programming and Computer Software 2014 Vol. 40 No. 5 P. 288-295
A framework for fast text analysis, which is developed as a part of the Texterra project, is described. Texterra provides a scalable solution for the fast text processing on the basis of novel methods that exploit knowledge extracted from the Web and text documents. For the developed tools, details of the project, use cases, and ...
Added: November 26, 2017
Toldova S., Lyashevskaya O., Вопросы языкознания 2014 № 1 С. 120-145
This paper is an overview of the current issues and tendencies in Computational linguistics. The overview is based on the materials of the conference on computational linguistics COLING’2012. The modern approaches to the traditional NLP domains such as pos-tagging, syntactic parsing, machine translation are discussed. The highlights of automated information extraction, such as fact extraction, ...
Added: October 15, 2013
Fenogenova A., Karpov I., Kazorin V., , in : Proceedings of the Artificial Intelligence and Natural Language AINL FRUCT 2016 Conference, Saint-Petersburg, Russia, 10-12 November 2016. : FRUCT Oy, 2016. P. 31-36.
With the process of globalization the number of borrowings from English has rapidly increased in languages all over the world. In systems of automatic speech recognition, spell-checking, tagging and other tasks in the field of natural language processing the loan words frequently cause problems and should be treat separately. In this paper we present a ...
Added: October 19, 2016
Popkov Y., Dubnov Y. A., Volkovich Z. et al., Entropy 2017 Vol. 19(4) No. 178 P. 1-14
A proposal for a new method of classification of objects of various nature, named “2”-soft classification, which allows for referring objects to one of two types with optimal entropy probability for available collection of learning data with consideration of additive errors therein. A decision rule of randomized parameters and probability density function (PDF) is formed, ...
Added: May 26, 2017
Ekaterinburg : CEUR Workshop Proceedings, 2014
AIST'2014 is an international data science conference on Analysis of Images, Social Networks, and Texts. Traditionally, the conference is held annually in Yekaterinburg, Russia. The conference is intended for computer scientists and practitioners whose research interests involve Internet mathematics and other related fields of data science.
LIST OF TOPICS (NON EXHAUSTIVE)
Applications of Data Mining and Machine ...
Added: August 28, 2014
Феста Ю. Ю., Воробьев И. А., Model Assisted Statistics and Applications 2022 Vol. 17 No. 1 P. 41-49
We currently see a large increase in e-commerce sector; it is becoming a central trend in the banking industry. Fraudsters keep up with modern technologies, and use weak points in human psychology and security systems to steal money from regular users. To ensure the required level of security, banks began to apply artificial intelligence in ...
Added: April 13, 2022
Suvorova A., Смирнова К. Р., Будин Е. А. et al., Компьютерные инструменты в образовании 2018 № 3 С. 49-64
The article describes a student research project on predicting the class of a post on a social network based on its textual content. The features of the project are discussed as an integral part of the trajectory of teaching data analysis methods, including text analysis methods and tools that are often not included in machine ...
Added: January 28, 2019
Alimova l., Tutubalina E., Journal of Biomedical Informatics 2020 Vol. 103 P. 1-9
Relation extraction aims to discover relational facts about entity mentions from plain texts. In this work, we focus on clinical relation extraction; namely, given a medical record with mentions of drugs and their attributes, we identify relations between these entities. We propose a machine learning model with a novel set of knowledge-based and BioSentVec embedding ...
Added: October 28, 2020
Kitov V. V., Экономика, статистика и информатика. Вестник УМО 2016 № 4 С. 22-26
Gradient boosting method with random rotations is considered, where before training each base learner random rotation is applied to the feature space. The accuracy metric of the given method is estimated for a broad range of generated problems of binary classification. Obtained results are evaluated and recommendations given for application of this method. ...
Added: August 23, 2016
Kashnitsky Y., Kuznetsov S., , in : Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2016). : M. : [б.и.], 2016. P. 105-112.
Decision tree learning is one of the most popular classifica- tion techniques. However, by its nature it is a greedy approach to finding a classification hypothesis that optimizes some information-based crite- rion. It is very fast but may lead to finding suboptimal classification hy- potheses. Moreover, in spite of decision trees being easily interpretable, ensembles ...
Added: October 6, 2016
Gerasimenko Ekaterina, Puzhaeva Svetlana, Zakharova Elena et al., , in : Proceedings of Third Workshop "Computational linguistics and language science". Issue 4.: Manchester : EasyChair, 2019. P. 61-69.
In this paper, we address the problem of automatic extraction of discourse formulae. By discourse formulae (DF) we mean a special type of constructions at the discourse level, which have a fixed form and serve as a typical response in the dialogue. Unlike traditional constructions [4, 5, 6], they do not contain variables within the ...
Added: October 31, 2019
Tikhonova M., Elina Telesheva, Mirzoev S. et al., , in : 2021 International Conference Engineering and Telecommunication (En&T). : IEEE, 2022. P. 1-6.
Style transfer is an important and a rapidly developing of Natural Language Processing. This days more and more methods and models are proposed which allow us to generate text in predefined style. In this paper we propose a framework for style transfer of “Friends” TV series. The trained models are able to mimic one of ...
Added: May 21, 2022
Денис Турдаков, Астраханцев Н. А., Недумов Я. Р. et al., Труды Института системного программирования РАН 2014 Т. 26 С. 421-438
he paper presents a framework for fast text analytics developed during the Texterra project. Texterra is a technology for multilingual text mining based on novel text processing methods that exploit knowledge extracted from user-generated content. It delivers a fast scalable solution for text mining without the expensive customization. Depending on use-cases Texterra could be utilized ...
Added: November 6, 2017
Bartunov S., Кондрашкин Д. А., Osokin A. et al., / Arxiv.org. Series arXiv:1502.07257 "Computation and language". 2015.
Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take into account word ambiguity and maintain only single representation per word. Although a number of Skip-gram modifications were proposed to ...
Added: November 5, 2015
Krylov V., Krylov S., Journal of Physics: Conference Series 2018 Т. 1117 № conference 1
Reservoir Computing (RC) is taking attention of neural networks structures developers because of machine learning algorithms are simple at the high level of generalization of the models. The approaches are numerous. RC can be applied to different architectures including recurrent neural networks with irregular connections that are called Echo State Networks (ESN). However, the existence ...
Added: November 15, 2018