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Research of heuristic approaches for determining the tonality of text messages in natural language processing problems
P. 159-164.
Polyakov E. V., Polyakov S. V., Abramov P.
Determining the tonality of the text is a difficult task, the solution of which essentially depends on the context, the field of study and the amount of text data. The analysis shows that the authors in their works do not jointly use the full range of possible transformations on the data and their combinations. The article explores a generalized approach, which consists in sequentially passing through the stages of intelligence analysis, obtaining a basic solution, vectorization, preprocessing, tuning hyperparameters and modeling. The experiments carried out by iterative application of these stages give a positive increase in quality for classical machine learning algorithms and a significant increase for deep learning.
Polyakov E. V., Voskov L., Abramov P. et al., Informatsionno-upravliaiushchie sistemy [Information and Control Systems] 2020 No. 1 P. 2-14
Introduction: Sentiment analysis is a complex problem whose solution essentially depends on the context, field of study and amount of text data. Analysis of publications shows that the authors often do not use the full range of possible data transformations and their combinations. Only a part of the transformations is used, limiting the ways to ...
Added: February 20, 2020
Nikolaev K., Malafeev A., , in : Analysis of Images, Social Networks and Texts. 7th International Conference AIST 2018. : Springer, 2018. Ch. 12. P. 121-126.
This paper deals with automatic classification of questions in the Russian language. In contrast to previously used methods, we introduce a convolutional neural network for question classification. We took advantage of an existing corpus of 2008 questions, manually annotated in accordance with a pragmatic 14-class typology. We modified the data by reducing the typology to ...
Added: February 15, 2019
Latyshev P. N., Pavlov F., Frontiers in Big Data 2023 P. 1-10
Due to advances in NGS technologies whole-genome maps of various functional genomic elements were generated for a dozen of species, however experiments are still expensive and lacking for many species of interest. Deep learning methods became the state-of the art computational methods but are often species-specific, reflecting the data used to train them. Here we take ...
Added: January 11, 2023
Switzerland : Springer, 2019
This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Processing, IDP 2016, held in Barcelona, Spain, in October 2016.
The 11 revised full papers were carefully reviewed and selected from 52 submissions. The papers of this volume are organized in topical sections on machine learning theory with applications; intelligent data processing in life ...
Added: February 8, 2020
Piscataway : IEEE, 2020
2020 International Joint Conference on Neural Networks (IJCNN) held virtually, as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020. IJCNN 2020 is jointly organized by the IEEE Computational Intelligence Society (CIS) and the International Neural Network Society (INNS). For IJCNN 2020 (and when WCCI is organized in even-numbered years) IEEE CIS ...
Added: October 15, 2020
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
Soshnikov D. V., Valieva Y., Microsoft Journal of Applied Research, USA 2019 Vol. 12 P. 140-150
In this paper, we present a new Python library called mPyPl, which is intended to simplify complex data processing tasks using a functional approach. This library defines operations on lazy data streams of named dictionaries represented as generators (so-called multi-field datastreams), and allows enriching those data streams with more ’fields’ in the process of data ...
Added: November 20, 2020
Tutubalina E., Алимова И. С., Мифтахутдинов З. et al., Bioinformatics 2021 Vol. 37 No. 2 P. 243-249
Drugs and diseases play a central role in many areas of biomedical research and healthcare. Aggregating knowledge about these entities across a broader range of domains and languages is critical for information extraction (IE) applications. To facilitate text mining methods for analysis and comparison of patient’s health conditions and adverse drug reactions reported on the ...
Added: January 13, 2021
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
Shpilman A., Malysheva A., Belyaev V., , in : Proceedings of 2019 XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY). : IEEE, 2019. P. 165-170.
The task object tracking is vital in numerous applications such as autonomous driving, intelligent surveillance, robotics, etc. This task entails the assigning of a bounding box to an object in a video stream, given only the bounding box for that object on the first frame. In 2015, a new type of video object tracking (VOT) ...
Added: July 15, 2020
Andrey Okhotin, Dmitry Molchanov, Arkhipkin V. et al., , in : Advances in Neural Information Processing Systems 36 (NeurIPS 2023). : Curran Associates, Inc., 2023. P. 10038-10067.
Added: February 15, 2024
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
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
Atanov A., Ashukha A., Struminsky K. et al., , in : Proceedings of the 7th International Conference on Learning Representations (ICLR 2019). : ICLR, 2019. P. 1-17.
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploit generative models to encourage a specific structure of ...
Added: September 2, 2019
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
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
Malafeev A., Nikolaev K., , in : Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers. Communications in Computer and Information Science. Vol. 1086.: Springer, 2020. P. 154-159.
In this paper, a deep learning method study is conducted to solve a new multiclass text classification problem, identifying user interests by text messages. We used an original dataset of almost 90 thousand forum text messages, labeled for ten interests. We experimented with different modern neural network architectures: recurrent and convolutional, as well as simpler ...
Added: November 7, 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
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
Durandin O., Hilal N., Strebkov D. et al., , in : Proceedings of the ISMW-FRUCT 2016. : [б.и.], 2016. P. 90-93.
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. ...
Added: January 17, 2017
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
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
Golovanov S., Tselousov A., Rauf Kurbanov et al., , in : The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations. : Springer, 2020. P. 295-315.
Added: February 20, 2021