-
1Academic Journal
Συγγραφείς: Anatoliy Y. Poletaev, Ilya V. Paramonov, Elena I. Boychuk, Анатолий Юрьевич Полетаев, Илья Вячеславович Парамонов, Елена Игоревна Бойчук
Πηγή: Modeling and Analysis of Information Systems; Том 31, № 4 (2024); 362-383 ; Моделирование и анализ информационных систем; Том 31, № 4 (2024); 362-383 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: constituency tree, sentiment detection, sentiment towards an aspect, implicit aspect, semantic rules, publicism
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/1895/1403; B. Liu, Sentiment Analysis and Opinion Mining. Springer, 2022.; W. Zhang, X. Li, Y. Deng, L. Bing, and W. Lam, “A survey on aspect-based sentiment analysis: Tasks, methods, and challenges,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11019–11038, 2022, doi:10.1109/TKDE.2022.3230975.; O. Alqaryouti and others, “Aspect-based sentiment analysis using smart government review data,” Applied Computing and Informatics, vol. 20, no. 1/2, pp. 142–161, 2024, doi:10.1016/j.aci.2019.11.003.; A. Nazir, Y. Rao, L. Wu, and L. Sun, “Issues and challenges of aspect-based sentiment analysis: A comprehensive survey,” IEEE Transactions on Affective Computing, vol. 13, no. 2, pp. 845–863, 2020, doi:10.1109/TAFFC.2020.2970399.; A. Poletaev, I. Paramonov, and E. Kolupaev, “Methods of implicit aspect detection in Russian publicism sentences,” Modeling and Analysis of Information Systems, vol. 31, no. 3, pp. 226–239, 2024, doi:10.18255/1818-1015-2024-3-226-239.; A. D. Kazun, “Construction of social problems in the media and agenda-setting theory: the limits of concepts' compatibility,” Monitoring of Public Opinion: Economic and Social Changes, no. 3 (133), pp. 159–172, 2016, doi:10.14515/monitoring.2016.3.09.; A. I. Guseva, I. A. Kuznetsov, P. V. Bochkarev, and D. S. Smirnov, “DIGITAL SHADOW OF Russian INTERNATIONAL MEGAPROJECTSB OF NPP CONSTRUCTION ABROAD: ASSESSMENT OF THE TONE OF UTTERANCES,” Modern High Technologies, no. 12 (1), pp. 26–34, 2022, doi:10.17513/snt.39432.; W. Zhang, X. Li, Y. Deng, L. Bing, and W. Lam, “A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11019–11038, 2023, doi:10.1109/TKDE.2022.3230975.; E. G. Brunova, Y. V. Bidulya, and A. A. Gorbunov, “Aspect-based sentiment analysis of political discourse,” Tyumen State University Herald. Humanities Research. Humanitates, vol. 7, no. 3 (27), pp. 6–22, 2021, doi:10.21684/2411-197X-2021-7-3-6-22.; Muljono, B. Harjo, and R. Abdullah, “Aspect-Based Sentiment Analysis for Financial Review with Implicit Aspect and Opinion Using Semantic Similarity and Hybrid Approach,” International Journal of Intelligent Engineering & Systems, vol. 17, no. 5, pp. 646–658, 2024, doi:10.22266/ijies2024.1031.49.; K. Ananthajothi, K. Karthikayani, and R. Prabha, “Explicit and implicit oriented Aspect-Based Sentiment Analysis with optimal feature selection and deep learning for demonetization in India,” Data & Knowledge Engineering, vol. 142, p. 102092, 2022, doi:10.1016/j.datak.2022.102092.; C. Hutto and E. Gilbert, “VADER: A parsimonious rule-based model for sentiment analysis of social media text,” in Proceedings of the International AAAI Conference on Web and Social Media, 2014, vol. 8, no. 1, pp. 216–225.; N. Chechneva, “Simple and Efficient Approach to the Aspect Extraction from Customers' Product Reviews,” in Proceedings of the 26th Conference of Open Innovations Association FRUCT, 2020, pp. 67–73, doi:10.23919/fruct48808.2020.9087546.; A. Poletaev, I. Paramonov, and E. Boychuk, “Automatic Detection of Sentiment Towards Explicit Aspect in Russian Publicism Sentences Using Syntactic Structure,” in Proceedings of the 36th Conference of Open Innovations Association FRUCT, 2024, pp. 593–602.; M. A. Pil'gun, “Rechevye osobennosti politicheskoj kommunikacii,” Proceedings of Kazan University. Humanities Sciences Series, vol. 152, no. 2, pp. 236–246, 2010.; Y. Song and others, “Targeted Sentiment Classification with Attentional Encoder Network,” in Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series, 2019, pp. 93–103, doi:10.1007/978-3-030-30490-4_9.; J. Ansel and others, “PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation,” in ASPLOC'24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, 2024, pp. 929–947, doi:10.1145/3620665.3640366.; A. Naumov and others, “Neural-network method for determining text author’s sentiment to an aspect specified by the named entity,” in CEUR Workshop Proceedings, 2020, vol. 2648, pp. 134–143.; Y. Wang, L. Wu, J. Li, X. Liang, and M. Zhang, “Are the BERT family zero-shot learners? A study on their potential and limitations,” Artificial Intelligence, vol. 322, p. 103953, 2023, doi:10.1016/j.artint.2023.103953.; A. Golubev, N. Rusnachenko, and N. Loukachevitch, “RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts,” in Computational Linguistics and Intellectual Technologies: Papers from the Annual conference “Dialogue” (2023), 2023, vol. 22, pp. 130–141, doi:10.28995/2075-7182-2023-22-130-141.; D. Ma and others, “Interactive attention networks for aspect-level sentiment classification,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017, pp. 4068–4074, doi:10.5555/3171837.3171854.; A. Y. Poletaev, I. V. Paramonov, and E. I. Boychuk, “Semantic rule-based sentiment detection algorithm for Russian publicism sentences,” Modeling and Analysis of Information Systems, vol. 30, no. 4, pp. 394–417, 2023, doi:10.18255/1818-1015-2023-4-394-417.; A. Y. Poletaev, I. V. Paramonov, and E. I. Boychuk, “Algorithm of constituency tree from dependency tree construction for a Russian-language sentence,” Informatics and Automation, vol. 22, no. 6, pp. 1323–1353, 2023, doi:10.15622/ia.22.6.3.; D. Chandrasekaran and V. Mago, “Evolution of semantic similarity — a survey,” ACM Computing Surveys (CSUR), vol. 54, no. 2, pp. 1–37, 2021, doi:10.1145/3440755.; A. Kutuzov and E. Kuzmenko, “WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models,” in Analysis of Images, Social Networks and Texts: 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7--9, 2016, Revised Selected Papers, Cham: Springer International Publishing, 2017, pp. 155–161.; M. Korobov, “Morphological Analyzer and Generator for Russian and Ukrainian Languages,” in Analysis of Images, Social Networks and Texts, vol. 542, Springer International Publishing, 2015, pp. 320–332.; P. Qi and others, “Stanza: A Python Natural Language Processing Toolkit for Many Human Languages,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2020, pp. 101–108, doi:10.18653/v1/2020.acl-demos.14.
-
2Academic Journal
Συγγραφείς: Maksim A. Kosterin, Ilya V. Paramonov, Максим Алексеевич Костерин, Илья Вячеславович Парамонов
Πηγή: Modeling and Analysis of Information Systems; Том 31, № 1 (2024); 90-101 ; Моделирование и анализ информационных систем; Том 31, № 1 (2024); 90-101 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: BERT, sarcasm detection, neural network-based classifier, deep learning, natural language processing
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/1840/1388; M. Kosterin, I. Paramonov, and N. Lagutina, “Automatic Irony and Sarcasm Detection in Russian Sentences: Baseline Methods,” in 33rd Conference of Open Innovations Association FRUCT, 2023, pp. 148–154, doi:10.23919/FRUCT58615.2023.10142992.; D. vSandor and M. B. Babac, “Sarcasm detection in online comments using machine learning,” Information Discovery and Delivery, 2023, doi:10.1108/IDD-01-2023-0002.; R. A. Potamias, G. Siolas, and A.-G. Stafylopatis, “A transformer-based approach to irony and sarcasm detection,” Neural Computing and Applications, vol. 32, pp. 17309–17320, 2020, doi:10.1007/s00521-020-05102-3.; C. Van Hee, E. Lefever, and V. Hoste, “Semeval-2018 task 3: Irony detection in English tweets,” in Proceedings of The 12th International Workshop on Semantic Evaluation, 2018, pp. 39–50, doi:10.18653/v1/S18-1005.; M. Khodak, N. Saunshi, and K. Vodrahalli, “A large self-annotated corpus for sarcasm.” 2017.; E. Riloff, A. Qadir, P. Surve, L. De Silva, N. Gilbert, and R. Huang, “Sarcasm as contrast between a positive sentiment and negative situation,” in Proceedings of the 2013 conference on empirical methods in natural language processing, 2013, pp. 704–714.; S. Zhang, X. Zhang, J. Chan, and P. Rosso, “Irony detection via sentiment-based transfer learning,” Information Processing & Management, vol. 56, no. 5, pp. 1633–1644, 2019, doi:10.1016/j.ipm.2019.04.006.; D. Hazarika, S. Poria, S. Gorantla, E. Cambria, R. Zimmermann, and R. Mihalcea, “Cascade: Contextual sarcasm detection in online discussion forums.” 2018.; T. Zefirova and N. Loukachevitch, “Irony and sarcasm expression in Twitter,” EPiC Series in Language and Linguistics, vol. 4, pp. 45–49, 2019, doi:10.29007/tpzw.; A. A. Gurin and T. A. Zhukov, “Avtomaticheskoe opredelenie sarkazma v tekstakh na russkom yazyke,” Tsyfrovaya ekonomika, vol. 1(22), pp. 44–53, 2023.; A. D. Yacoub, S. Slim, and A. Aboutabl, “A Survey of Sentiment Analysis and Sarcasm Detection: Challenges, Techniques, and Trends,” International journal of electrical and computer engineering systems, vol. 15, no. 1, pp. 69–78, 2024, doi:10.32985/ijeces.15.1.7.; Y. Kuratov and M. Arkhipov, “Adaptation of deep bidirectional multilingual transformers for Russian language.” 2019.; D. Zmitrovich et al., “A family of pretrained transformer language models for Russian.” 2023.; C. Zhou, C. Sun, Z. Liu, and F. Lau, “A C-LSTM neural network for text classification.” 2015.; A. Rogers, A. Romanov, A. Rumshisky, S. Volkova, M. Gronas, and A. Gribov, “RuSentiment: An enriched sentiment analysis dataset for social media in Russian,” in Proceedings of the 27th international conference on computational linguistics, 2018, pp. 755–763.
-
3Academic Journal
Συγγραφείς: Anatoliy Y. Poletaev, Ilya V. Paramonov, Egor M. Kolupaev, Анатолий Юрьевич Полетаев, Илья Вячеславович Парамонов, Егор Михайлович Колупаев
Πηγή: Modeling and Analysis of Information Systems; Том 31, № 3 (2024); 226-239 ; Моделирование и анализ информационных систем; Том 31, № 3 (2024); 226-239 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: publicism, implicit aspects, sentiment analysis
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/1876/1397; B. Liu, Sentiment Analysis and Opinion Mining. Springer, 2022.; W. Zhang, X. Li, Y. Deng, L. Bing, and W. Lam, “A survey on aspect-based sentiment analysis: Tasks, methods, and challenges,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11019–11038, 2022, doi:10.1109/TKDE.2022.3230975.; M. M. Trucscǎ and F. Frasincar, “Survey on aspect detection for aspect-based sentiment analysis,” Artificial Intelligence Review, vol. 56, no. 5, pp. 3797–3846, 2023.; A. Naumov, R. Rybka, A. Sboev, A. Selivanov, and A. Gryaznov, “Neural-network method for determining text author's sentiment to an aspect specified by the named entity,” in CEUR Workshop Proceedings, 2020, vol. 2648, pp. 134–143.; E. V. Sergeeva, “Features of speech exposure in the preelection media discourse,” in Aktual'nye problemy gumanitarnogo znaniya v tekhnicheskom vuze, 2021, pp. 237–239.; A. Nazir, Y. Rao, L. Wu, and L. Sun, “Issues and challenges of aspect-based sentiment analysis: A comprehensive survey,” IEEE Transactions on Affective Computing, vol. 13, no. 2, pp. 845–863, 2020, doi:10.1109/TAFFC.2020.2970399.; P. K. Soni and R. Rambola, “A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and Scope,” IEEE Access, vol. 10, pp. 63932–63957, 2022, doi:10.1109/ACCESS.2022.3183205.; B. Mohammed and others, “Hybrid approach to extract adjectives for implicit aspect identification in opinion mining,” in 11th International Conference on Intelligent Systems: Theories and Applications (SITA), 2016, pp. 1–5, doi:10.1109/SITA.2016.7772284.; A. O. Kornej and E. N. Kryuchkova, “Semantiko-statisticheskij algoritm opredeleniya kategorij aspektov v zadachah sentiment-analiza,” Izvestiya Yuzhnogo federal'nogo universiteta. Tekhnicheskie nauki, no. 6 (216), pp. 66–74, 2020, doi:10.18522/2311-3103-2020-6-66-74.; E. I. Gribkov and Y. P. Ekhlakov, “Nejrosetevaya model' na osnove sistemy perekhodov dlya izvlecheniya sostavnyh ob'ektov i ih atributov iz tekstov na estestvennom yazyke,” Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, vol. 23, no. 1, pp. 47–52, 2020, doi:10.21293/1818-0442-2020-23-1-47-52.; L. Hickman, S. Thapa, L. Tay, M. Cao, and P. Srinivasan, “Text preprocessing for text mining in organizational research: Review and recommendations,” Organizational Research Methods, vol. 25, no. 1, pp. 114–146, 2022, doi:10.1177/1094428120971683.; S. Bird, E. Klein, and E. Loper, Natural language processing with Python: analyzing text with the natural language toolkit. O'Reilly Media, Inc., 2009.; U. Naseem, I. Razzak, and P. W. Eklund, “A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on Twitter,” Multimedia Tools and Applications, vol. 80, pp. 35239–35266, 2021, doi:10.1007/s11042-020-10082-6.; J. Coates and D. Bollegala, “Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), 2018, pp. 194–198, doi:10.18653/v1/N18-2031.; T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space.” 2013.; I. Yamada et al., “Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2020, pp. 23–30, doi:10.18653/v1/2020.emnlp-demos.4.; A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, “Bag of Tricks for Efficient Text Classification,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, 2017, pp. 427–431, doi:10.48550/arXiv.1607.01759.; A. Kukushkin, “Navec -- kompaktnye embeddingi dlya russkogo yazyka.” 2020, Accessed: Aug. 11, 2024. [Online]. Available: https://natasha.github.io/navec/.; J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global Vectors for Word Representation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1532–1543, doi:10.3115/v1/D14-1162.; Q. Le and T. Mikolov, “Distributed representations of sentences and documents,” in International conference on machine learning, 2014, pp. 1188–1196.; F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
-
4Academic Journal
Συγγραφείς: Anatoliy Y. Poletaev, Ilya V. Paramonov, Elena I. Boychuk, Анатолий Юрьевич Полетаев, Илья Вячеславович Парамонов, Елена Игоревна Бойчук
Συνεισφορές: The reported study was funded by the grant of Russian Science Foundation No. 23-21-00495., Исследование выполнено за счет гранта Российского научного фонда №23-21-00495.
Πηγή: Modeling and Analysis of Information Systems; Том 30, № 4 (2023); 394-417 ; Моделирование и анализ информационных систем; Том 30, № 4 (2023); 394-417 ; 2313-5417 ; 1818-1015
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/1828/1382; B. Liu, Sentiment Analysis and Opinion Mining. Springer, 2022.; A. Dvoybikova, A. Karpov, and O. Verkholyak, “Analytical Review of Methods for Identifying Emotions in Text Data,” in 3rd International Conference on R. Piotrowski's Readings in Language Engineering and Applied Linguistics, PRLEAL 2019, 2020, pp. 8–21.; S. Smetanin and M. Komarov, “Deep Transfer Learning Baselines for Sentiment Analysis in Russian,” Information Processing & Management, vol. 58, no. 3, p. 102484, 2021.; K. Nursakitov, A. Bekishev, S. Kumargazhanova, and A. Urkumbaeva, “Review of Methods for Determining the Tonation of Texts in Natural Languages,” Bulletin of Shakarim University. Technical Sciences, no. 1 (9), pp. 59–67, 2023.; M. S. Bacsarslan and F. Kayaalp, “Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach,” Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 1, pp. 35–49, 2021.; M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A Survey on Sentiment Analysis Methods, Applications, and Challenges,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5731–5780, 2022.; E. N. Tulupova and E. V. Golovina, “Lexico-Stylistic Percularities of Tourist's Internet Commentary,” Philology. Theory & Practice, vol. 12, no. 5, pp. 257–261, 2019.; E. I. Boychuk, “Lexical and Grammatical Features of Internet Reviews in the Russian and English Languages,” Verhnevolzhski Philological Bulletin, no. 3 (26), pp. 107–115, 2021.; A. Y. Poletaev and I. V. Paramonov, “Recursive sentiment detection algorithm for Russian sentences,” Automatic Control and Computer Sciences, vol. 57, no. 7, pp. 740–749, 2023.; M. A. Eremina, “Rechevoj zhanr otzyva v kommunikativnom prostranstve interneta,” Nauchnyj dialog, no. 5 (53), pp. 34–45, 2016.; A. R. Kalashnikova, “Informativnaya Tekstovaya Tonal'nost' Kak Opredelyayushchij Faktor Ritmicheskoj Tekstovoj Organizacii,” Izvestiya Volgogradskogo Gosudarstvennogo Pedagogicheskogo Universiteta, vol. 3 (107), pp. 113–116, 2016.; I. V. Paramonov and A. Y. Poletaev, “Annotation of Text Corpora by Sentiment and Presence of Irony within a Project of Citizen Science,” Modelirovanie i Analiz Informatsionnykh Sistem, vol. 30, no. 1, pp. 86–100, 2023.; N. Loukachevitch and A. Levchik, “Creating a General Russian Sentiment Lexicon,” in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), 2016, pp. 1171–1176.; D. Kulagin, “Publicly Available Sentiment Dictionary for the Russian Language KartaSlovSent,” in Computational Linguistics and Intellectual Technologies: Proceesings of the Annual “Dialog” Conference (2021), 2021, pp. 1106–1119.; A. Y. Poletaev, I. V. Paramonov, and E. I. Boychuk, “Algorithm of Constituency Tree from Depencency Tree Construction for a Russian-Language Sentence,” Informatics and Automation, vol. 22, no. 6, pp. 1323–1353, 2023.; L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Routledge, 2017.; O. Koltsova, S. Alexeeva, S. Pashakhin, and S. Koltsov, “PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media,” in Conference on Artificial Intelligence and Natural Language, 2020, pp. 1–16.; W. Souma, I. Vodenska, and H. Aoyama, “Enhanced News Sentiment Analysis Using Deep Learning Methods,” Journal of Computational Social Science, vol. 2, no. 1, pp. 33–46, 2019.; A. B. Junior, N. F. F. da Silva, T. C. Rosa, and C. G. C. Junior, “Sentiment Analysis with Genetic Programming,” Information Sciences, vol. 562, pp. 116–135, 2021.
-
5Academic Journal
Συγγραφείς: Ilya Vyacheslavovich Paramonov, Anatoliy Yurievich Poletaev, Илья Вячеславович Парамонов, Анатолий Юрьевич Полетаев
Πηγή: Modeling and Analysis of Information Systems; Том 30, № 1 (2023); 86-100 ; Моделирование и анализ информационных систем; Том 30, № 1 (2023); 86-100 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: citizen science, text corpus, statistical analysis, agreement measures
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/1768/1363; V. Masoumi, M. Salehi, H. Veisi, G. Haddadian, V. Ranjbar, and M. Sahebdel, “TeleCrowd: A Crowdsourcing Approach to Create Informal to Formal Text Corpora.” 2020.; E. Mitiagina, M. Borodataya, E. Volchenkova, N. Ershova, M. Luchinina, and E. Kotelnikov, “Russian Text Corpus of Intimate Partner Violence: Annotation Through Crowdsourcing,” in 7th International Conference on Electronic Governance and Open Society: Challenges in Eurasia. EGOSE 2020, Springer, 2020, pp. 306–321.; S. Mohammad, “A practical guide to sentiment annotation: Challenges and solutions,” in Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis, 2016, pp. 174–179.; S. M. Mohammad, P. Sobhani, and S. Kiritchenko, “Stance and Sentiment in Tweets,” Special Section of the ACM Transactions on Internet Technology on Argumentation in Social Media, vol. 17, no. 3, pp. 1–23, 2017.; B. R. Chakravarthi, V. Muralidaran, R. Priyadharshini, and J. P. McCrae, “Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text,” in Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), 2020, pp. 202–210.; K. Krippendorff, Content analysis: an introduction to its methodology. Thousand Oaks, CA: SAGE Publications, Inc., 2013.; Y. Zhao, B. Qin, and T. Liu, “Creating a fine-grained corpus for chinese sentiment analysis,” IEEE Intelligent Systems, vol. 30, no. 1, pp. 36–43, 2014.; J. Cohen, “A coefficient of agreement for nominal scales,” Educational and psychological measurement, vol. 20, no. 1, pp. 37–46, 1960.; J. Bu et al., “ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 2069–2079.; M. Navas-Loro, V. Rodr'iguez-Doncel, I. Santana-Perez, and A. S'anchez, “Spanish Corpus for Sentiment Analysis Towards Brands,” in Speech and Computer. SPECOM 2017, 2017, pp. 680–689.; J. L. Fleiss, “Measuring nominal scale agreement among many raters,” Psychological bulletin, vol. 76, no. 5, p. 378, 1971.; A. Rogers, A. Romanov, A. Rumshisky, S. Volkova, M. Gronas, and A. Gribov, “RuSentiment: An enriched sentiment analysis dataset for social media in Russian,” in Proceedings of the 27th international conference on computational linguistics, 2018, pp. 755–763.; T. V. Zherebilo, Slovar lingvisticheskih terminov. Nazran: OOO Piligrim, 2010.; K. Krippendorff, “Computing Krippendorff's Alpha-Reliability.” 2008, Accessed: Jan. 17, 2023. [Online]. Available: https://repository.upenn.edu/asc_papers/43/.; J. Hughes, “krippendorffsalpha: An R package for measuring agreement using Krippendorff's alpha coefficient,” The R Journal, vol. 13, no. 1, pp. 413–425, 2021.; L. A. Jeni, J. F. Cohn, and F. De La Torre, “Facing imbalanced data--recommendations for the use of performance metrics,” in 2013 Humaine association conference on affective computing and intelligent interaction, 2013, pp. 245–251.; A. Y. Poletaev and I. V. Paramonov, “Recursive sentiment detection algorithm for Russian sentences,” Modelirovanie i Analiz Informatsionnykh Sistem, vol. 29, no. 2, pp. 134–147, 2022.; S. Smetanin and M. Komarov, “Deep transfer learning baselines for sentiment analysis in Russian,” Information Processing & Management, vol. 58, no. 3, p. 102484, 2021.; R. Artstein and M. Poesio, “Inter-coder agreement for computational linguistics,” Computational linguistics, vol. 34, no. 4, pp. 555–596, 2008.
-
6Academic Journal
Συγγραφείς: Vladislav Dmitrievich Larionov, Ilya Vyacheslavovich Paramonov, Владислав Дмитриевич Ларионов, Илья Вячеславович Парамонов
Πηγή: Modeling and Analysis of Information Systems; Том 29, № 3 (2022); 266-279 ; Моделирование и анализ информационных систем; Том 29, № 3 (2022); 266-279 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: новостные статьи, automatic text processing, subject area, Russian language, news articles, автоматическая обработка текстов, предметная область, русский язык
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/1716/1325; A. Hussain, G. Ali, F. Akhtar, Z. H. Khand, and A. Ali, “Design and analysis of news category predictor”, Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6380-6385, 2020.; G. Kaur and K. Bajaj, “News classification using neural networks”, Communications on applied electronics, vol. 5, no. 1, pp. 42-45, 2016.; P. Semberecki and H. Maciejewski, “Deep learning methods for subject text classification of articles”, in 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE, 2017, pp. 357-360.; X. Luo, “Efficient English text classification using selected machine learning techniques”, Alexandria Engineering Journal, vol. 60, no. 3, pp. 3401-3409, 2021.; S. Vychegzhanin, E. Kotelnikov, and V. Milov, “Comparative analysis of machine learning methods for news categorization in Russian”, in CEUR Workshop Proceedings, vol. 2922, 2021, pp. 100-108.; N. A. Gordienko, “Klassifikaciya novostej s primeneniem metodov mashinnogo obucheniya i obrabotki estestvennogo yazyka”, in Innovacionnye resheniya social’nyh, ekonomicheskih i tekhnologicheskih problem sovremennogo obshchestva, in Russian, 2021, pp. 63-65.; E. N. Karuna and P. V. Sokolov, “Comparison of methods for automatic classification of Russian-language texts”, in Journal of Physics: Conference Series, vol. 1864, 2021, p. 012 117.; J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, 2018. arXiv: 1810.04805 [cs.CL].; F. Pedregosa, G. Varoquaux, A. Gramfort, et al., “Scikit-learn: Machine learning in Python”, the Journal of machine Learning research, vol. 12, pp. 2825-2830, 2011.; T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, 2013. arXiv: 1301.3781v3 [cs.CL].; R. Rˇ ehu˚rˇek and P. Sojka, “Software framework for topic modelling with large corpora”, in Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, 2010, pp. 45-50.; A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Je´gou, and T. Mikolov, Fasttext.zip: Compressing text classification models, 2016. arXiv: 1612.03651 [cs.CL].; K. S. Jones, “A statistical interpretation of term specificity and its application in retrieval”, Journal of documentation, vol. 28, no. 1, pp. 11-22, 1972.; T. Brown, B. Mann, N. Ryder, et al., “Language models are few-shot learners”, Advances in neural information processing systems, vol. 33, pp. 1877-1901, 2020.; T. Wolf, L. Debut, V. Sanh, et al., “Transformers: State-of-the-art natural language processing”, in Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, 2020, pp. 38-45.; M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks”, Information Processing & Management, vol. 45, pp. 427-437, 2009.
-
7Academic Journal
Συγγραφείς: Maksim A. Kosterin, Ilya V. Paramonov, Максим Алексеевич Костерин, Илья Вячеславович Парамонов
Πηγή: Modeling and Analysis of Information Systems; Том 29, № 2 (2022); 116-133 ; Моделирование и анализ информационных систем; Том 29, № 2 (2022); 116-133 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: natural language processing, neural network-based classifier, BERT
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/1650/1267; C. Potts, Z. Wu, A. Geiger, and D. Kiela, Dynasent: A dynamic benchmark for sentiment analysis, 2020. arXiv: 2012.15349 [cs.CL].; F. Hamborg and K. Donnay, “NewsMTSC: a dataset for (multi-) target-dependent sentiment classification in political news articles”, in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), 2021, pp. 1663-1675.; B. Liu, “Sentiment analysis and opinion mining”, Synthesis lectures on human language technologies, vol. 5, no. 1, pp. 1-167, 2012.; O. Habimana, Y. Li, R. Li, X. Gu, and G. Yu, “Sentiment analysis using deep learning approaches: an overview”, Science China Information Sciences, vol. 63, no. 1, pp. 1-36, 2020.; S. Smetanin and M. Komarov, “Deep transfer learning baselines for sentiment analysis in Russian”, Information Processing & Management, vol. 58, no. 3, p. 102 484, 2021.; A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners”, Technical report, OpenAI, 2019.; Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “XLNet: Generalized autoregressive pretraining for language understanding”, Advances in neural information processing systems, vol. 32, pp. 5754-5764, 2019.; J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018. arXiv: 1810.04805v2 [cs.CL].; N. Kalchbrenner, E. Grefenstette, and P. Blunsom, A convolutional neural network for modelling sentences, 2014. arXiv: 1404.2188 [cs.CL].; I. Paramonov and A. Poletaev, “Adaptation of Semantic Rule-Based Sentiment Analysis Approach for Russian Language”, in Proceedings of 30th Conference of Open Innovations Association FRUCT, IEEE, 2021, pp. 155-164.; K. Kenyon-Dean, E. Ahmed, S. Fujimoto, J. Georges-Filteau, C. Glasz, B. Kaur, A. Lalande, S. Bhanderi, R. Belfer, N. Kanagasabai, et al., “Sentiment analysis: It’s complicated!”, in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2018, pp. 1886-1895.; X. Tan, Y. Cai, and C. Zhu, “Recognizing conflict opinions in aspect-level sentiment classification with dual attention networks”, in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019, pp. 3426-3431.; M. Soleymani, D. Garcia, B. Jou, B. Schuller, S.-F. Chang, and M. Pantic, “A survey of multimodal sentiment analysis”, Image and Vision Computing, vol. 65, pp. 3-14, 2017.; L. A. M. Oberla¨nder and R. Klinger, “An analysis of annotated corpora for emotion classification in text”, in Proceedings of the 27th International Conference on Computational Linguistics, 2018, pp. 2104-2119.; A. Radford, R. Jozefowicz, and I. Sutskever, Learning to generate reviews and discovering sentiment, 2017. arXiv: 1704.01444v2 [cs.LG].; Y. Wang, M. Huang, L. Zhao, and X. Zhu, “Attention-based LSTM for aspect-level sentiment classification”, in Proceedings of the 2016 conference on empirical methods in natural language processing, 2016, pp. 606-615. Neural Network-Based Sentiment Classification of Russian Sentences into Four Classes; P. Chen, Z. Sun, L. Bing, and W. Yang, “Recurrent attention network on memory for aspect sentiment analysis”, in Proceedings of the 2017 conference on empirical methods in natural language processing, 2017, pp. 452-461.; A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need”, in Advances in neural information processing systems, 2017, pp. 5998-6008.; R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank”, in Proceedings of the 2013 conference on empirical methods in natural language processing, 2013, pp. 1631-1642.; S. Smetanin and M. Komarov, “Sentiment Analysis of Product Reviews in Russian using Convolutional Neural Networks”, in IEEE 21st Conference on Business Informatics (CBI), vol. 1, 2019, pp. 482-486.
-
8Academic Journal
Συγγραφείς: Anatoliy Y. Poletaev, Ilya V. Paramonov, Анатолий Юрьевич Полетаев, Илья Вячеславович Парамонов
Πηγή: Modeling and Analysis of Information Systems; Том 29, № 2 (2022); 134-147 ; Моделирование и анализ информационных систем; Том 29, № 2 (2022); 134-147 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: sentiment corpus, sentiment detection, semantic rules
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/1651/1268; I. Paramonov and A. Poletaev, “Adaptation of Semantic Rule-Based Sentiment Analysis Approach for Russian Language”, in Proceedings of 30th Conference of Open Innovations Association FRUCT, 2021, pp. 155-164.; T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing contextual polarity in phrase-level sentiment analysis”, in Proceedings of human language technology conference and conference on empirical methods in natural language processing, 2005, pp. 347-354.; L. K.-W. Tan, J.-C. Na, Y.-L. Theng, and K. Chang, “Sentence-level sentiment polarity classification using a linguistic approach”, in International Conference on Asian Digital Libraries, 2011, pp. 77-87.; Y. Xie, Z. Chen, K. Zhang, Y. Cheng, D. K. Honbo, A. Agrawal, and A. N. Choudhary, “MuSES: multilingual sentiment elicitation system for social media data”, IEEE Intelligent Systems, vol. 29, no. 4, pp. 34-42, 2014.; S. Smetanin and M. Komarov, “Deep transfer learning baselines for sentiment analysis in Russian”, Information Processing & Management, vol. 58, no. 3, p. 102 484, 2021.; M. A. M. Shaikh, H. Prendinger, and M. Ishizuka, “Sentiment assessment of text by analyzing linguistic features and contextual valence assignment”, Applied Artificial Intelligence, vol. 22, no. 6, pp. 558-601, 2008.; O. Appel, F. Chiclana, J. Carter, and H. Fujita, “A hybrid approach to the sentiment analysis problem at the sentence level”, Knowledge-Based Systems, vol. 108, pp. 110-124, 2016.; S. Kahane and N. Mazziotta, “Syntactic Polygraphs. A Formalism Extending Both Constituency and Dependency”, in Proceedings of the 14th Meeting on the Mathematics of Language, 2015, pp. 152-164. Recursive Sentiment Detection Algorithm for Russian Sentences; Y. Gao, J.-G. Lou, and D. Zhang, A Hybrid Semantic Parsing Approach for Tabular Data Analysis, 2019. arXiv: 1910.10363v2 [cs.AI].; J. Li, H. Tan, and M. Bansal, “Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information”, in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 1041-1050.; Z. Marji, A. Nighojkar, and J. Licato, “Probing the Natural Language Inference Task with Automated Reasoning Tools”, in The Thirty-Third International Flairs Conference, 2020.; R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank”, in Proceedings of the 2013 conference on empirical methods in natural language processing, 2013, pp. 1631-1642.; K. S. Tai, R. Socher, and C. D. Manning, “Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks”, in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2015.; Y. Zhang and Y. Zhang, “Tree communication models for sentiment analysis”, in Proceedings of the 57th annual meeting of the association for computational linguistics, 2019, pp. 3518-3527.; D. Yin, T. Meng, and K.-W. Chang, “SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics”, in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 3695-3706.; N. V. Loukachevitch and A. V. Levchick, “Creating a General Russian Sentiment Lexicon”, in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), 2016, pp. 1171-1176.
-
9Academic Journal
Συγγραφείς: Nadezhda S. Lagutina, Ksenia V. Lagutina, Aleksey S. Adrianov, Ilya V. Paramonov, Надежда Станиславовна Лагутина, Ксения Владимировна Лагутина, Алексей Сергеевич Адрианов, Илья Вячеславович Парамонов
Πηγή: Modeling and Analysis of Information Systems; Том 25, № 4 (2018); 435-458 ; Моделирование и анализ информационных систем; Том 25, № 4 (2018); 435-458 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: выделение ключевых слов, semantic relationships, automatic thesaurus construction, automatic relationship extraction, keyword extraction, семантические отношения, автоматическое построение тезауруса, автоматическое выделение связей
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/735/552; Aitchison J., Gilchrist A. and Bawden D., Thesaurus construction and use: a practical manual, Psychology Press, 2000, 230 pp.; Сидорова Е. А., “Подход к моделированию процесса извлечения информации из текста на основе онтологии”, Онтология проектирования, 8:1(27) (2018), 134– 151; Еленевская М. Н., Овчинникова И. Г., “Хранение и описание вербальных ассоциаций: словари и тезаурусы”, Вопросы психолингвистики, 2016, № 29, 69–92; Paramonov I. et al., “Thesaurus-Based Method of Increasing Text-via-Keyphrase Graph Connectivity During Keyphrase Extraction for e-Tourism Applications”, Communications in Computer and Information Science, 649, Springer, 2016, 129–141.; Shchitov I., Lagutina K., Lagutina N., Paramonov I., “Sentiment classification of long newspaper articles based on automatically generated thesaurus with various semantic relationships”, Proceedings of the 21st Conference of Open Innovations Association FRUCT, University of Helsinki, Helsinki, Finland, 2017, 290–295.; Бленда Н. А., “Обзор русскоязычных тезаурусов для решения задачи расчета семантической близости между научными публикациями”, Информационные технологии и системы, Труды Четвертой Международной научной конференции, 2015, 70–74; Поршнев С. В., “О качестве открытых электронных тезаурусов русского языка”, Сборник материалов Всероссийской молодежной школы-семинара «Актуальные проблемы информационных технологий, электроники и радиотехники – 2015» (ИТЭР –2015), 2 (2015), 45–48; Loukachevitch N., Dobrov B., “RuThes linguistic ontology vs. Russian wordnets”, Proceedings of the Seventh Global WordNet Conference, 2014, 154–162.; Loukachevitch N., Dobrov B., Chetviorkin I., “RuThes-Lite, a publicly available version of Thesaurus of Russian language RuThes”, Computational Linguistics and Intellectual Technologies: papers from the Annual conference ”Dialogue”, 2014, № 13(20), 340–349.; Loukachevitch N. V., Lashevich G., Gerasimova A. A., Ivanov V. V., Dobrov B. V., “Creating Russian WordNet by conversion”, Computational Linguistics and Intellectual Technologies: papers from the Annual conference ”Dialogue”, 2016, № 15(22), 405–415.; Braslavski P., Ustalov D., Mukhin M., Kiselev Y., “YARN: Spinning-in-Progress”, Proceedings of the Eight Global Wordnet Conference, 2016, 58–65.; Сухоногов А. М., Яблонский С. А., “Автоматизация построения англо-русского WordNet”, Компьютерная лингвистика и интеллектуальные технологии, Труды Международного семинара "Диалог", 2005, 25–31; Azarowa I., “RussNet as a computer lexicon for Russian”, Proceedings of the Intelligent Information systems IIS-2008, 2008, 341–350.; Азарова И. В., Захаров В. П., Киселев Ю., Усталов Д. А., Хохлова М. В., “Интеграция тезаурусов RussNet и YARN”, Компьютерная лингвистика и вычислительные онтологии, Труды XIX Международной объединённой научной конференции «Интернет и современное общество» (IMS-2016), Санкт-Петербург, 22–24 июня 2016 г., Университет ИТМО, СПб, 2016, 7–13; Сладкова О., Пирумова Л., Пирумов А., “Информационные ресурсы Интернет для специалистов сельского хозяйства”, Международный сельскохозяйственный журнал, 2016, № 2, 44–48; Галиева А. М., Якубова Д. Д., “Принципы представления лексики в общественнополитическом тезаурусе татарского языка”, Филологические науки. Вопросы теории и практики, 2016, № 12-2 (66), 80–84; Галиева А. М., Кириллович А. В., Лукашевич Н. В., Невзорова О. А., Сулейманов Д.Ш., Якубова Д. Д., “Русско-татарский общественно-политический тезаурус: публикация в облаке лингвистических открытых связанных данных”, International Journal of Open Information Technologies, 5:11 (2017), 64–73; Агеев М. С., Добров Б. В., Лукашевич Н. В., “Автоматическая рубрикация текстов: методы и проблемы”, Учён. зап. Казан. гос. ун-та. Сер. Физ.-матем. науки, 150:4 (2008), 25–40; Лукашевич Н. В., Добров Б. В., Павлов А. М., Штернов С. В., “Онтологические ресурсы и информационно-аналитическая система в предметной области «Безопасность»”, Онтология проектирования, 8:1 (27) (2018), 74–95; Мишунин О. Б., Савинов А. П., Фирстов Д. И., “Проблемы, возникающие в интеллектуальных обучающих системах при оценке ответов на естественном языке”, Современные проблемы науки и образования, 2015, № 2–2, 189–199; Алексеев А. А., “Тематический анализ новостного кластера как основа тематического аннотирования”, Программная инженерия, 2014, № 3, 41–48; Усталов Д. А., “Обнаружение понятий в графе синонимов”, Вычислительные технологии, 22:S1 (2017), 99–112; Kolchin M., Chistyakov A., Lapaev M., Khaydarova R., “FOODpedia: Russian food products as a linked data dataset”, International Semantic Web Conference, 2015, 87– 09.; Hasan K., Vincent N., “Automatic keyphrase extraction: A survey of the state of the art”, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014, 1262–1273.; Добров Б. В., Лукашевич Н. В., “Лингвистическая онтология по естественным наукам и технологиям для приложений в сфере информационного поиска”, Учен. зап. Казан. гос. ун-та. Сер. Физ.-матем. науки, 149:2 (2007), 49–72; Лукашевич Н. В., Добров Б. В., Чуйко Д. С., “Отбор словосочетаний для словаря системы автоматической обработки текстов”, Компьютерная лингвистика и интеллектуальные технологии: Тр. Международной конференции "Диалог", 2008, № 7(14), 339–344; Turney P. D., Pantel P., “From frequency to meaning: Vector space models of semantics”, Journal of artificial intelligence research, 37 (2010), 141–188.; Захаров В. П., “Корпусно-ориентированный подход к построению тезаурусов и онтологий”, Структурная и прикладная лингвистика, 2015, № 11, 123–141; Котова Е. Е., Писарев И. А., “Построение тематических онтологий с применением метода автоматизированной разработки тезаурусов”, Известия СПбГЭТУ «ЛЭТИ», 2016, № 3, 37–47; Аюшеева Н. Н., Кушеева Т. Н., “Способ вычисления весовых коэффициентов вершин семантической сети научного текста”, Фундаментальные исследования, 2012, № 6-3, 626–630; Аюшеева Н. Н., Гомбожапова Т. Н., Доржаев Т. В., “Способ автоматического определения тематики научного текста”, Фундаментальные исследования, 2016, № 8-2, 229–233; Chetviorkin I, Loukachevitch N., “Extraction of Russian sentiment lexicon for product meta-domain”, Proceedings of COLING 2012, 2012, 593–610. [33] Loukachevitch N., Levchik A., “Creating a General Russian Sentiment Lexicon”, Proceedings of Language Resources and Evaluation Conference, 2016, 1171–1176.; Ванюшкин А. С., Гращенко Л. А., “Оценка алгоритмов извлечения ключевых слов: инструментарий и ресурсы”, Новые информационные технологии в автоматизированных системах, 20 (2017), 95–102; Лукашевич Н. В., Логачев Ю. М., “Комбинирование признаков для автоматического извлечения терминов”, Вычислительные методы и программирование, 11:4 (2010), 108–116; Лагутина Н. С., Лагутина К. В., Мамедов Э. И., Парамонов И. В., “Методические аспекты выделения семантических отношений для автоматической генерации специализированных тезаурусов и их оценки”, Моделирование и анализ информационных систем, 23:6 (2016), 826–840; Лукашевич Н. В., “Квазисинонимы в лингвистических онтологиях”, Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции "Диалог", 2010, № 9(16), 307–312; Лукашевич Н. В., “Моделирование отношений ЧАСТЬ–ЦЕЛОЕ в лингвистическом ресурсе для информационно-поисковых приложений”, Информационные технологии, 2007, № 12, 28–34; Баранюк В. В., Богорадникова А. В., Смирнова О. С., “Определение семантического содержания предметной области на основе формирования тезауруса”, International Journal of Open Information Technologies, 4:9 (2016), 74–79; Нугуманова А. Б., Бессмертный И. А., Пецина П., Байбурин Е. М., “Обогащение модели Bag-of-Words семантическими связями для повышения качества классификации текстов предметной области”, Программные продукты и системы, 2016, № 2(114), 89– 99; Panchenko A., Ustalov D., Arefyev N., Paperno D., Konstantinova N., Loukachevitch N., Biemann C., “Human and machine judgements for russian semantic relatedness”, Analysis of Images, Social Networks and Texts. 5th International Conference, AIST 2016, Springer, 2016, 221–235.; Rapp R., “The automatic generation of thesauri of related words for English, French, German, and Russian”, International Journal of Speech Technology, 11:3–4 (2008), 147– 156.; Галина И. В., Козеренко Е. Б., Морозова Ю. И., Сомин Н. В., Шарнин М. М., “Ассоциативные портреты предметной области—инструмент автоматизированного построения систем big data для извлечения знаний: теория, методика, визуализация, возможное применение”, Информатика и её применения, 9:2 (2015), 92–110; Kuznetsov I. P., Kozerenko E. B., Charnine M. M., “Technological peculiarity of knowledge extraction for logical-analytical systems”, Proceedings of ICAI, 12, 2012, 18–21.; Золотарев О. В., Шарнин М. М., “Методы извлечения знаний из текстов естественного языка и построение моделей бизнес-процессов на основе выделения процессов, объектов, их связей и характеристик”, Труды Международной научной конференции CPT2014, 2015, 92–98; Золотарев О. В., Шарнин М. М., Клименко С. В., “Семантический подход к анализу террористической активности в сети Интернет на основе методов тематического моделирования”, Вестник Российского нового университета. Серия: Сложные системы: модели, анализ и управление, 2016, № 3, 64–71; Лагутина Н. С., Лагутина К. В., Щитов И. А., Парамонов И. В., “Анализ использования различных типов связей между терминами тезауруса, сгенерированного с помощью гибридных методов, в задачах классификации текстов”, Моделирование и анализ информационных систем, 24:6 (2017), 772–787; Sabirova K., Lukanin A., “Automatic Extraction of Hypernyms and Hyponyms from Russian Texts”, Supplementary Proceedings of the 3rd International Conference on Analysis of Images, Social Networks and Texts (AIST’2014), 2014, 35–40.; Большакова Е. И., Иванов К. М., Сапин А. С., Шариков Г. Ф., “Система для извлечения информации из текстов на базе лексико-синтаксических шаблонов”, Пятнадцатая национальная конференция по искусственному интеллекту с международным участием, 2016, 14–22; Рабчевский Е. А., “Автоматическое построение онтологий на основе лексикосинтаксических шаблонов для информационного поиска”, Электронные библиотеки: перспективные методы и технологии, электронные коллекции, сб. науч. тр. 11-й Всероссийской научной конференции RCDL-2009, Петрозаводск, 2009, 69–77; Mihalcea R., Tarau P., “TextRank: Bringing order into texts”, Proceedings of Empirical Methods in Natural Language Processing – EMNLP, ACL, Barcelona, Spain, 2004, 404– 411.; Wiemer-Hastings P., Wiemer-Hastings K., Graesser A., “Latent semantic analysis”, Proceedings of the 16th international joint conference on Artificial intelligence, 2004, 1–14.; Noh S., Kim S., Jung C., “A Lightweight Program Similarity Detection Model using XML and Levenshtein Distance”, FECS, 2006, 3–9.; Lefever E., Van de Kauter M., Hoste V., “Evaluation of automatic hypernym extraction from technical corpora in English and Dutch”, 9th International Conference on Language Resources and Evaluation (LREC), 2014, 490–497.
-
10Academic Journal
Συγγραφείς: Nadezhda S. Lagutina, Ksenia V. Lagutina, Ivan A. Shchitov, Ilya V. Paramonov, Надежда Станиславовна Лагутина, Ксения Владимировна Лагутина, Иван Андреевич Щитов, Илья Вячеславович Парамонов
Πηγή: Modeling and Analysis of Information Systems; Том 24, № 6 (2017); 772-787 ; Моделирование и анализ информационных систем; Том 24, № 6 (2017); 772-787 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: классификация по тональности, semantic relations, thesaurus relations, topical classification, sentiment classification, семантические отношения, тезаурусные связи, тематическая классификация
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/614/478; Masterman M., “Semantic message detection for machine translation, using an interlingua”, Proc. 1961 International Conf. on Machine Translation, 1961, 438–475.; Loukachevitch N., Dobrov B., “The Sociopolitical Thesaurus as a resource for automatic document processing in Russian”, Terminology, 21:2 (2015), 237–262.; Aitchison J., Clarke S.D., “The thesaurus: a historical viewpoint, with a look to the future”, Cataloging and classification quarterly, 37:3–4 (2004), 5–21.; Лукашевич Н. В., Тезаурусы в задачах информационного поиска, Издательство МГУ, М., 2011, 512 с.; Willis C., Losee R., “A random walk on an ontology: Using thesaurus structure for automatic subject indexing”, Journal of the American Society for Information Science and Technology, 64:7 (2013), 1330–1344.; V´allez M., Pedraza-Jim´enez R., Codina L., Blanco S., Rovira C, “A semi-automatic indexing system based on embedded information in HTML documents”, Library Hi Tech, 33:2 (2015), 195–210.; Loukachevitch N., Nokel M., Ivanov K., Combining Thesaurus Knowledge and Probabilistic Topic Models, 2017, https://arxiv.org/abs/1707.09816.; Sanchez-Pi N., Mart´ı L. Garcia A. C. B., “Improving ontology-based text classification: An occupational health and security application”, Journal of Applied Logic, 17 (2016), 48–58.; Bollegala D., Weir D., Carroll J., “Cross-domain sentiment classification using a sentiment sensitive thesaurus”, IEEE transactions on knowledge and data engineering, 25:8 (2013), 1719–1731.; Sparck Jones K., Walker S., Robertson S.E., “A probabilistic model of information retrieval: development and comparative experiments: Part 2”, Information Processing and Management, 36:6 (2000), 809–840.; Лагутина Н. С., Лагутина К. В., Мамедов Э. И., Парамонов И. В., “Методические аспекты выделения семантических отношений для автоматической генерации специализированных тезаурусов и их оценки”, Моделирование и анализ информационных систем, 23:6 (2016), 826–840; Mihalcea R., Tarau P., “TextRank: Bringing order into texts”, Proceedings of Empirical Methods in Natural Language Processing – EMNLP, ACL, Barcelona, Spain, 2004, 404– 411.; Trieschnigg D., Pezik P., Lee V., De Jong F., Kraaij W., Rebholz-Schuhmann D., “MeSH Up: effective MeSH text classification for improved document retrieval”, Bioinformatics, 25:11 (2009), 1412–1418.; Aggarwal C., Zhai C., “A survey of text classification algorithms”, Mining text data, Springer-Verlag, New York, 2012, 163–222.; Grimmer J., Stewart B., “Text as data: The promise and pitfalls of automatic content analysis methods for political texts”, Political analysis, 21:3 (2013), 267–297.; Ravi K., Ravi V., “A survey on opinion mining and sentiment analysis: tasks, approaches and applications”, Knowledge-Based Systems, 89 (2015), 14–46.; Junker M., Hoch R., Dengel A., “On the evaluation of document analysis components by recall, precision, and accuracy”, Proceedings of the Fifth International Conference on Document Analysis and Recognition, IEEE, 1999, 713–716.
-
11Academic Journal
Συγγραφείς: S. I. Balandin, A. M. Vasilev, N. I. Kozhemyakin, D. A. Laure, I. V. Paramonov, Сергей Игоревич Баландин, Андрей Михайлович Васильев, Никита Ильич Кожемякин, Денис Александрович Лаурэ, Илья Вячеславович Парамонов
Συνεισφορές: Федеральная целевая программа «Научные и научно-педагогические кадры инновационной России на 2009–2013 годы»
Πηγή: Modeling and Analysis of Information Systems; Том 19, № 5 (2012); 131-141 ; Моделирование и анализ информационных систем; Том 19, № 5 (2012); 131-141 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: управление параллелизмом, Smart-M3, Internet of things, Parallelism control, интернет вещей
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/62/60; Atzori L., Iera A., Morabito G. The internet of things: A survey // Computer Networks. — 2010. — Vol. 54, no. 15. — P. 2787–2805.; Internet of Things Strategic Research Roadmap / O. Vermesan, P. Friess, P. Guillemin et al. // Internet of Things — Global Technological and Societal Trends. — 2011. — P. 9–52.; User Scenarios 2020 — a Worldwide Wireless Future / L. Sørensen, K. E. Skouby, D. Dietterle et al. // WWRF Outlook: Visions and Research Directions for the Wireless World. — 2009. — July. — no. 4.; The Internet of Things Initiative. — URL: http://www.iot-i.eu/public/front-page.; TiViT Internet of Things. — URL: http://www.internetofthings.fi.; Internet-of-Things Architecture. Deliverable D1.3 — Updated reference model for IoT v1.5. — URL: http://www.iot-a.eu/public/public-documents/documents-1/1/1/copy_of_d1.2/at_download/file.; Kok J. A fully abstract semantics for data flow nets // PARLE Parallel Architectures and Languages Europe / Springer. — 1987. — P. 351–368.; Smart-M3 information sharing platform / J. Honkola, H. Laine, R. Brown, O. Tyrkk¨o // 2010 IEEE Symposium on Computers and Communications (ISCC) / IEEE. — 2010. — P. 1041–1046.; Luukkala V., Honkola J. Integration of an answer set engine to Smart-M3 // Smart Spaces and Next Generation Wired/Wireless Networking. — 2010. — P. 92–101.; Case study: Context-aware supervision of a smart maintenance process / S. PantsarSyvaniemi, E. Ovaska, S. Ferrari et al. // Proceedings of the 11th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT 2011) / IEEE. — 2011. — P. 309–314.; Solaiman K., Morgan G. Later validation/earlier write: Concurrency control for resource-constrained systems with real-time properties // Proceedings of 2011 30th IEEE Symposium on Reliable Distributed Systems Workshops (SRDSW) / IEEE. — 2011. — October. — P. 9–12.; H¨arder T. Observations on optimistic concurrency control schemes // Information Systems. — 1984. — Vol. 9, no. 2. — P. 111–120.; Concurrency control in mobile environments: Issues & chalenges / S. A. Moiz, S. N. Pal, J. Kumar et al. // International Journal of Database Management Systems (IJDMS). — 2011. — November. — Vol. 3, no. 4. — P. 147–159.
-
12Academic Journal
Συγγραφείς: D. A. Laure, N. S. Lagutina, I. V. Paramonov, Денис Александрович Лаурэ, Надеджа Станиславовна Лагутина, Илья Вячеславович Парамонов
Συνεισφορές: Фонд содействия развитию малых форм предприятий в научно-технической сфере
Πηγή: Modeling and Analysis of Information Systems; Том 21, № 4 (2014); 91-103 ; Моделирование и анализ информационных систем; Том 21, № 4 (2014); 91-103 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: пульс, pulse detection, heart rate, mobile phone, camera, мобильный телефон, камера
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/101/99; Karvonen J., Vuorimaa T. et al. Heart rate and exercise intensity during sports activities. practical application // Sports medicine (Auckland, NZ). 1988. Vol. 5, No. 5. P. 303.; Laukkanen R. M., Virtanen P. K. Heart rate monitors: state of the art // Journal of Sports Sciences. 1998. Vol. 16, No. sup1. P. 3–7.; Laure D. Heart rate measuring using mobile phone’s camera // Proceedings of the 12th Conference of Open Innovations Association FRUCT and Seminar on e-Travel. Oulu, Finland, November 5–9, 2012. St.-Petersburg : SUAI, 2012. P. 272–273.; Dantu R. Measuring Vital Signs Using Smart Phones: Ph. D. thesis. University of North Texas, 2010.; A simple algorithm to monitor hr for real time treatment applications / K. Banitsas, P. Pelegris, T. Orbach et al. // Information Technology and Applications in Biomedicine, 2009. ITAB 2009. 9th International Conference on / IEEE. 2009. P. 1–5.; Tanaka H., Monahan K. D., Seals D. R. Age-predicted maximal heart rate revisited // Journal of the American College of Cardiology. — 2001. Vol. 37, No. 1. P. 153–156.
-
13Academic Journal
Συγγραφείς: A. M. Vasilev, I. V. Paramonov, N. S. Lagutina, E. I. Mamedov, Андрей Михайлович Васильев, Илья Вячеславович Парамонов, Надежда Станиславовна Лагутина, Эльдар Интизамович Мамедов
Συνεισφορές: Федеральная целевая программа «Научные и научно-педагогические кадры инновационной России на 2009–2013 годы»
Πηγή: Modeling and Analysis of Information Systems; Том 20, № 4 (2013); 81-90 ; Моделирование и анализ информационных систем; Том 20, № 4 (2013); 81-90 ; 2313-5417 ; 1818-1015
Θεματικοί όροι: платформа Smart-M3, dataflow network, information flow integrity, Smart-M3 platform, dataflow-сеть, целостность информационных потоков
Περιγραφή αρχείου: application/pdf
Relation: https://www.mais-journal.ru/jour/article/view/186/196; Atzori L., Iera A., Morabito G. The internet of things: A survey // Computer Networks. 2010. Vol. 54, No 15. P. 2787–2805.; Sensor-enabled rfid system for monitoring arm activity: Reliability and validity / Barman Joydip, Uswatte Gitendra, Ghaffari Touraj et al. // IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2012. Vol. 20, No 6. P. 771–777.; The energy aware smart home / Marco Jahn, Marc Jentsch, Christian R. Prause et al. // 5th International Conference on Future Information Technology (FutureTech). 2010. P. 1–8.; Song J.-h., Hou S.-f. Infrared application in smart home system—based on intelligent air conditioning design // Proceedings of 3rd International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012) / Ed. by Runliang Dou. Springer Berlin Heidelberg, 2013. P. 721–728.; Chen Y.-K. Challenges and opportunities of internet of things // 17th Asia and South Pacific Design Automation Conference (ASP-DAC). 2012. February. P. 383–388.; Synthesizing hardware from dataflow programs / Jörn W Janneck, Ian D Miller, David B Parlour et al. // Journal of Signal Processing Systems. 2011. Vol. 63, No 2. P. 241–249.; Vasilev A., Paramonov I., Balandin S. Mechanism for robust dataflow operation on smart spaces // Proceedings of the 12th Conference of Open Innovations Association FRUCT and Seminar on e-Travel. Oulu, Finland, November 5-9, 2012. St.-Petersburg : SUAI, 2012. P. 154–164.; Smart-M3 information sharing platform / J. Honkola, H. Laine, R. Brown, O. Tyrkkö // IEEE Symposium on Computers and Communications (ISCC) / IEEE. 2010. P. 1041–1046.; Klyne G., Carroll J., McBride B. Resource description framework (rdf):concepts and abstract syntax. 2004. February. URL: http://www.w3.org/TR/2004/REC-rdf-concepts-20040210/.; Schneider M., Carroll J., Herman J., Patel-Schneider P. Owl 2 web ontology language rdf-based semantics (second edition). 2011. December. URL:http://www.w3.org/TR/2012/REC-owl2-rdf-based-semantics-20121211/.; RedSib: a Smart-M3 semantic information broker implementation / F. Morandi, L. Roffia, A. D’Elia et al. // Proceedings of the 12th Conference of Open Innovations Association FRUCT and Seminar on e-Tourism. Oulu, Finland. St.-Petersburg : SUAI, 2012. November. P. 86–98.