Ekaterina Artemova
- Associate Professor:Faculty of Computer Science / Big Data and Information Retrieval School
- Ekaterina Artemova has been at HSE University since 2010.
Education and Degrees
- 2016
Candidate of Sciences* (PhD) in Computer Science
- 2012
Master's
HSE University - 2010
Bachelor's in Mathematical modeling
HSE University, School of applied mathematics and infromation science Doctoral programme
HSE University
According to the International Standard Classification of Education (ISCED) 2011, Candidate of Sciences belongs to ISCED level 8 - "doctoral or equivalent", together with PhD, DPhil, D.Lit, D.Sc, LL.D, Doctorate or similar. Candidate of Sciences allows its holders to reach the level of the Associate Professor.
Young Faculty Support Program (Group of Young Academic Professionals)
Category "New Researchers" (2013)
Student Term / Thesis Papers
- Bachelor
M. Bogdashevskaya, POS Tagging With Transfer Learning. Faculty of Computer Science, 2020
D. Maduar, Multidocument Summarization Applied to Drug Reviews. Faculty of Computer Science, 2020
E. Chuprun, Exam Test Solving Using Machine Learning Techniques. Faculty of Computer Science, 2020
N. Palchikov, Multidocument Summarization. Faculty of Computer Science, 2020
A. Sumekenov, Bot Detection in Social Media. Faculty of Computer Science, 2020
D. Burshtein, Multidocument Summarization. Faculty of Computer Science, 2020
A. Rodigina, Natural Language Processing Meets Computational Social Science. Faculty of Computer Science, 2020
I. Trofimova, Dynamic Topic Modelling for Semantic Shift Detection. Faculty of Computer Science, 2020
A. Kim, Modelling Causal Reasoning in Language: Detecting Counterfactuals. Faculty of Computer Science, 2020
T. Petrov, A Study of Attitude towards the LGBTQ+ Community with Natural Language Processing Methods. Faculty of Computer Science, 2020
P. Zhizhin, Lifelong Learning for NLP Tasks. Faculty of Computer Science, 2020
K. Lotfullin, iOS Application RuSlang - Dictionary of Russian Slang. Faculty of Computer Science, 2019
K. Vaniev, Usage of Media Sentiment Analysis for Forecasting Inflation. Faculty of Economic Sciences, 2019
S. Dymchenko, Language Recognition Using Multimodal Deep Learning. Faculty of Mathematics, 2019
E. Svitanko, Multiple-choice Question Answering in the Russian Language. Faculty of Computer Science, 2019
D. Puzyrev, Supervised Approaches for Detection of Non-Compositional Nominal Compounds. Faculty of Computer Science, 2019
P. Khrushkov, Morphological Reinfection. Faculty of Computer Science, 2019
D. Popov, Comparison of Sentence Embeddings for Natural Language Understanding in Russian. Faculty of Mathematics, 2019
M. Bakhanova, Paraphrase Identification on Russian. HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE), 2019
A. Pugachev, Machine Reading Methods for Answering Multiple Choice Questions. Faculty of Computer Science, 2019
V. Goriachko, Morphological Reinfection. Faculty of Computer Science, 2019
M. Florinskiy, Text Punctuation Restoration. Faculty of Computer Science, 2019
P. Sviatokum, Machine Reading Methods for Answering Multiple Choice Questions. Faculty of Computer Science, 2019
A. Efimov, Using Methods of Sentiment Analysis to Predict GDP Growth on Media Materials. Faculty of Economic Sciences, 2019
T. Glushkova, Character Level Models for Hashtag Segmentation. Faculty of Computer Science, 2018
V. Sarkisyan, Using Syntactic Information for Stock Prices Prediction. Faculty of Computer Science, 2018
A. Son, Extraction of Structured Data from News Reports for Predicting Financial Indicators. Faculty of Computer Science, 2018
A. Bratchuk, Development of a Hashtag Segmentation System in Russian. HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE), 2018
K. Avetisyan, Slang Detection and Normalization. Faculty of Computer Science, 2018
A. Kudinov, Text Summarization Using Deep Learning. Faculty of Computer Science, 2018
P. Kondratenkov, Text Summarization Using Deep Learning. Faculty of Computer Science, 2018
M. Pershin, Building A Predictive Model for Valuing Securities By Analyzing Unstructured News Information. Faculty of Economic Sciences, 2018
K. Kulpina, Extracting Social Networks from Literary Fiction. Faculty of Computer Science, 2017
V. Provatorova, Using Lexical and Syntactical Patterns for Generating Reviews in Russian Language. Faculty of Computer Science, 2016
A. Shishkova, Annotated Suffix Tree Method for German Compound Splitting. Faculty of Computer Science, 2016
G. Kotov, Comparison of Word Representation Method for Obscene Language Removal. Faculty of Computer Science, 2016
N. Eremeeva, Cognitive Psychology Term Analysis with the Help of Computational Techniques. Faculty of Computer Science, 2016
R. Bobrov, Comparison of Key Word and Phrase Visualization Technique. Faculty of Computer Science, 2016
A. Puksant, Named Entity Recognition from Russian Texts. Faculty of Computer Science, 2016
- Master
V. Fomin, Discourse Probing of Transformer-Based Language Models. Faculty of Humanities, 2020
I. Dolgaleva, Detecting Adverse Drug Reactions from Microblogs. Faculty of Computer Science, 2020
M. Eydlina, Transfer Learning for Semantic Uncertainty Detection Tasks. Faculty of Computer Science, 2020
A. Chikina, Detecting Propaganda Techniques in News Articles. Faculty of Computer Science, 2020
T. Glushkova, DaNetQA: A Dataset for Binary Question Answering in Russian. Faculty of Computer Science, 2020
V. Sarkisyan, Entity and Relation Extraction from Government Documents. Faculty of Computer Science, 2020
O. Matkarimov, Lifelong Learning for NLP Tasks. Faculty of Computer Science, 2020
A. Kolomiets, Developing of NLP Services for News Agency. Faculty of Computer Science, 2019
I. Krotova, Neural Networks Approach to Splitting German Compounds. Faculty of Humanities, 2019
K. Samoilenko, Automatic detection of anxiety disorders in text data. Faculty of Humanities, 2019
B. Fabre, Using Active Learning in Text Classification Tasks. Faculty of Computer Science, 2018
T. Balzhanov, Hashtag Segmentation into Constituent Words. Faculty of Computer Science, 2018
M. Ponomareva, Active Learning in Morphology Inflection. Faculty of Humanities, 2018
E. Chudakova, Active Learning for Sentiment Analysis. Faculty of Computer Science, 2018
A. Zakharov, Creation of Tonality Dictionaries using the Word Proximity Graph. Faculty of Computer Science, 2018
K. Milintsevich, Automatic Code-Switching Detection in Texts in Minor Languages of Russia. Faculty of Humanities, 2018
A. Selepov, Infornation Extraction from Financial News for Market Indices Forecast. Faculty of Humanities, 2018
A. Marakasova, Extraction and Analysis of Time Evolving Topics. Faculty of Humanities, 2017
A. Voitekhovich, Creation of a Sentiment Lexicon from Domain Social Media for Sentiment Analysis. Faculty of Humanities, 2017
D. Kaiutenko, Automatic Analysis of Sentiment Changes in News Texts with Emergence of New Information. Faculty of Humanities, 2017
V. Tushkanov, Impact of Morphological Preprocessing of Training Corpus on Quality of Russian Distributional Semantic Models. Faculty of Humanities, 2017
Courses (2020/2021)
- Unstructured Data Analysis (Bachelor’s programme; Faculty of Computer Science; 4 year, 1, 2 module)Rus
- Past Courses
Courses (2019/2020)
Research Problems in Natural Language Processing (Postgraduate course’s programme; Faculty of Computer Science; field of study "09.06.01. Информатика и вычислительная техника", field of study "02.06.01. Компьютерные и информационные науки"; 2 year, 1 semester)Eng
- Text Analysis. Generative Models (Master’s programme; Faculty of Computer Science; 2 year, 1, 2 module)Rus
Unstructured Data Analysis (Bachelor’s programme; Faculty of Computer Science; field of study "01.03.02. Прикладная математика и информатика", field of study "01.03.02. Прикладная математика и информатика"; 4 year, 1, 2 module)Rus
Courses (2018/2019)
Research Problems in Natural Language Processing (Postgraduate course’s programme; Faculty of Computer Science; field of study "09.06.01. Информатика и вычислительная техника", field of study "02.06.01. Компьютерные и информационные науки"; 1 year, 1 semester)Eng
Courses (2017/2018)
- Applied Data Analysis Problems (Minor; Faculty of Computer Science; 3, 4 module)Rus
- Machine Learning (Master’s programme; Faculty of Humanities; 1 year, 2, 3 module)Rus
Courses (2016/2017)
- Applied Data Analysis Problems (Minor; Faculty of Computer Science; 3, 4 module)Rus
- Machine Learning (Master’s programme; Faculty of Humanities; 1 year, 2, 3 module)Rus
Courses (2015/2016)
Data Analysis and Data Mining (Bachelor’s programme; Faculty of Computer Science; "Алгоритмика"; field of study "01.03.02. Прикладная математика и информатика"; 3 year, 1, 2 module)Eng
Research Seminar "Natural Language Processing" (Bachelor’s programme; Faculty of Computer Science; "Алгоритмика"; field of study "01.03.02. Прикладная математика и информатика"; 3 year, 1-4 module)Rus
- Research Seminar "Natural Language Processing" (Bachelor’s programme; Faculty of Computer Science; 4 year, 1-3 module)Rus
- Research Seminar "Natural Language Processing" (Bachelor’s programme; Faculty of Computer Science; 2 year, 1-4 module)Rus
Conferences
- 2015Workshop on Interactions between Data Mining and Natural Language Processing, DMNLP 2015 (Porto). Presentation: Some Thoughts on Using Annotated Suffix Trees for Natural Language Processing.
- Workshop on Interactions between Data Mining and Natural Language Processing, DMNLP 2015 (Porto). Presentation: Annotated Suffix Tree Similarity Measure for Text Summarization.
- 20142nd International Conference on Information Technology and Quantitative Management, ITQM 2014 (Москва). Presentation: A Method for Refining a Taxonomy by Using Annotated Suffix Trees and Wikipedia Resources.
- The 3rd International Conference on Analysis of Images, Social Networks, and Texts (AIST 2014) (Екатеринбург). Presentation: Conceptual Maps: Construction Over a Text Collection and Analysis.
Publications47
- Chapter Глушкова Т. О., Machnev A., Fenogenova A., Shavrina T., Artemova E., Ignatov D. I. DaNetQA: a yes/no Question Answering Dataset for the Russian Language, in: Recent Trends in Analysis of Images, Social Networks and Texts. 9th International Conference, AIST 2020. Revised Supplementary Proceedings / Ed. by W. M. van der Aalst, V. Batagelj, A. V. Buzmakov, D. I. Ignatov, A. A. Kalenkova, M. Khachay, O. Koltsova, A. Kutuzov, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch, I. Makarov, A. Napoli, A. Panchenko, P. M. Pardalos, M. Pelillo, A. Savchenko, E. Tutubalina. Vol. 1357. Springer, 2021. doi
- Chapter Ekaterina Artemova. Deep Learning for the Russian Language, in: The Palgrave Handbook of Digital Russia Studies. Palgrave Macmillan, 2021. P. 001-002. doi
- Chapter Artemova E. Deep Learning for the Russian Language, in: The Palgrave Handbook of Digital Russia Studies / Ed. by D. Gritsenko, M. Wijermars, M. Kopotev. Palgrave Macmillan, 2021. doi Ch. 26. P. 465-481. doi
- Chapter Krotova I., Aksenov S., Artemova E. A Joint Approach to Compound Splitting and Idiomatic Compound Detection, in: Proceedings of The 12th Language Resources and Evaluation Conference Vol. 12. European Language Resources Association (ELRA), 2020. P. 4410-4417.
- Article Ekaterina Artemova, Bakarov A., Artemov A., Burnaev E. V., Sharaev M. Data-driven models and computational tools for neurolinguistics: a language technology perspective // Journal of Cognitive Science. 2020. Vol. 1. No. 21. P. 15-52. doi
- Preprint Klyuchnikov N., Trofimov I., Artemova E., Salnikov M., Fedorov M., Burnaev E. V. NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing / arXiv. Series arXiv:[cs.LG] "arXiv:[cs.LG]". 2020.
- Chapter Ivanin V., Artemova E., Batura T., Ivanov V., Sarkisyan V., Tutubalina E., Smurov I. RUREBUS-2020 Shared Task: Russian Relation Extraction for Business, in: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2020) / Ed. by В. Селегей. -, 2020. P. 401-416.
- Chapter Artemova E., Batura T., Sarkisyan V., Tutubalina E., Smurov I. RuREBus-2020 Shared Task: Russian Relaton Extraction for Business, in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва, 17 июня — 20 июня 2020 г.) / Под общ. ред.: В. Селегей. Вып. 19(26). М. : Изд-во РГГУ, 2020. P. 416-432.
- Chapter Shavrina T., Fenogenova A., Emelyanov A., Shevelev D., Artemova E., Malykh V., Mikhailov V., Tikhonova M., Chertok A., Evlampiev A. RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2020. P. 4717-4726.
- Chapter Malykh V., Chernis K., Artemova E., Piontkovskaya I. SumTitles: a Summarization Dataset with Low Extractiveness, in: Proceedings of the 28th International Conference on Computational Linguistics. Barcelona, Spain: International Committee on Computational Linguistics, 2020. Ch. 503. P. 5718-5730.
- Chapter Logacheva V., Teslenko D., Shelmanov A., Remus S., Ustalov D., Kutuzov A. B., Artemova E., Biemann C., Ponzetto S. P., Panchenko A. Word Sense Disambiguation for 158 Languages using Word Embeddings Only, in: Proceedings of The 12th Language Resources and Evaluation Conference Vol. 12. European Language Resources Association (ELRA), 2020. P. 5943-5952.
- Chapter Puzyrev D., Shelmanov A., Panchenko A., Artemova E. A Dataset for Noun Compositionality Detection for a Slavic Language, in: Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing, 2019, Florence, Italy, Association for Computational Linguistics. Association for Computational Linguistics, 2019. P. 56-62. doi
- Chapter Artemova E., Harma A., Polyakov A. Active Learning for Conversational Interfaces in Healthcare Applications, in: Artificial Intelligence in Health. Springer Publishing Company, 2019. doi Ch. 3. P. 48-58. doi
- Chapter Popov D., Pugachev A., Svyatokum P., Svitanko E., Artemova E. Evaluation of Sentence Embedding Models for Natural Language Understanding Problems in Russian, in: Analysis of Images, Social Networks and Texts. 8th International Conference AIST 2019. Springer, 2019. P. 205-217. doi
- Chapter Emelyanov A., Artemova E. Gapping parsing using pretrained embeddings, attention mechanism and NCRF, in: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019) / Ed. by В. Селегей. Issue 18. M. : Russian State University for the Humanitie, 2019. P. 203-212.
- Chapter Emelyanov A., Artemova E. Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRF, in: Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing, 2019, Florence, Italy, Association for Computational Linguistics. Association for Computational Linguistics, 2019. P. 94-99. doi
- Chapter Puzyrev D. A., Shelmanov A., Panchenko A., Artemova E. Noun Compositionality Detection using Distributional Semantics for the Russian Language, in: Analysis of Images, Social Networks and Texts. 8th International Conference AIST 2019. Springer, 2019. P. 218-229. doi
- Book Wohlgenannt G., von Waldenfels R., Toldova S., Rakhilina E. V., Lyashevskaya O., Loukachevitch N. V., Artemova E. Proceedings of Third Workshop "Computational linguistics and language science", Issue 4. EasyChair, 2019. doi
- Chapter Алексейчук Н. Н., Sarkisyan V., Emelyanov A., Artemova E. Processing and Analysis of Russian Strategic Planning Programs, in: Digital Transformation and Global Society. Fourth International Conference, DTGS 2019, St. Petersburg, Russia, June 19–21, 2019, Revised Selected Papers / Ed. by Y. Kabanov. Springer, 2019. doi P. 68-81. doi
- Chapter Shishkova A., Artemova E. Annotated Suffix Tree Method for German Compound Splitting, in: CLLS 2016. Computational Linguistics and Language Science. Proceedings of the Workshop on Computational Linguistics and Language Science. Moscow, Russia, April 26, 2016 / Ed. by E. Artemova, D. Ilvovsky, D. Skorinkin, A. Vybornova. Vol. 1886. Aachen : CEUR Workshop Proceedings, 2017. P. 42-47.
- Chapter Пономарева М. А., Milintsevich K., Artemova E., Starostin A. Automated Word Stress Detection in Russian, in: Proceedings of the First Workshop on Subword and Character Level Models in NLP. Stroudsburg, PA : Association for Computational Linguistics, 2017. P. 31-35. doi
- Book CLLS 2016. Computational Linguistics and Language Science. Proceedings of the Workshop on Computational Linguistics and Language Science. Moscow, Russia, April 26, 2016 / Ed. by E. Artemova, D. Ilvovsky, D. Skorinkin, A. Vybornova. Vol. 1886. Aachen : CEUR Workshop Proceedings, 2017.
- Chapter Artemova E. Comparison of String Similarity Measures for Obscenity Filtering, in: Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing. Stroudsburg, PA : Association for Computational Linguistics, 2017. P. 97-101.
- Article Ильвовский Д. А., Артемова Е. Л. Глубинное обучение для автоматической обработки текстов // Открытые системы. СУБД. 2017. № 2. С. 26-29.
- Chapter Artemova E., Ilvovsky D. Annotated suffix trees for text clustering, in: The 3d International Workshop on Concept Discovery in Unstructured Data (CDUD 2016). Proceedings of the Third Workshop on Concept Discovery in Unstructured Data co-located with the 13th International Conference on Concept Lattices and Their Applications (CLA 2016), Moscow, Russia, July 18, 2016. CEUR Workshop Proceedings Vol. 1625. Aachen : CEUR Workshop Proceedings, 2016. P. 25-31.
- Chapter Wohlgenannt G., Artemova E., Ilvovsky D. Extracting social networks from literary text with word embedding tools, in: Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH). Osaka : , 2016. Ch. 4. P. 18-26.
- Chapter Galitsky B., Ilvovsky D., Artemova E., Kuznetsov S. Style and Genre Classification by Means of Deep Textual Parsing, in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва,1–4 июля 2016 г.) / Под общ. ред.: В. Селегей. Вып. 15. М. : Изд-во РГГУ, 2016. P. 171-181.
- Chapter Родин И. В., Artemova E., Dubov M., Mirkin B. Visualization of Dynamic Reference Graphs, in: Proceedings of TextGraphs-10: the Workshop on Graph-based Methods for Natural Language Processing. Stroudsburg, PA : Association for Computational Linguistics, 2016. P. 34-38.
- Article Artemova E., Mirkin B. Refining a Taxonomy by Using Annotated Suffix Trees and Wikipedia Resources // Annals of Data Science. 2015. Vol. 2. No. 1. P. 61-82.
- Chapter Artemova E. Some thoughts on using annotated suffix trees for Natural Language Processing, in: 2nd Workshop on Interactions Between Data Mining and Natural Language Processing, DMNLP 2015; Porto; Portugal; 7 September 2015 Issue 1410. Aachen : CEUR-WS, 2015. P. 5-18.
- Chapter Яковлев М. С., Артемова Е. Л. Using annotated suffix tree similarity measure for text summarisation // В кн.: Analysis of Large and Complex Data. Berlin : Springer, 2015. doi (in press)
- Chapter Ilvovsky D., Artemova E. Visualisation of Russian newspaper corpus by means of reference graphs, in: RuZA 2015 Workshop. Proceedings of Russian and South African Workshop on Knowledge Discovery Techniques Based on Formal Concept Analysis (RuZA 2015). November 30 - December 5, 2015, Stellenbosch, South Africa / Ed. by S. Kuznetsov, B. W. Watson. Vol. 1552. Aachen : CEUR Workshop Proceedings, 2015. P. 1-9.
- Chapter Artemova E. A Method for Refining a Taxonomy by Using Annotated Suffix Trees and Wikipedia Resources, in: Procedia Computer Science. 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. National Research University Higher School of Economics (HSE) in Moscow (Russia) on June 3-5, 2014 / Ed. by Y. Shi, A. Lepskiy, F. T. Aleskerov. Vol. 31. Amsterdam : Elsevier, 2014. Ch. 22. P. 193-200.
- Chapter Mirkin B., Artemova E. An AST method for scoring string-to-text similiarity in semantic text analysis, in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th Birthday / Ed. by F. T. Aleskerov, B. I. Goldengorin, P. M. Pardalos. Vol. 92. Berlin : Springer, 2014.
- Chapter Artemova E. An approach to the problem of annotation of research publications, in: Proceedings of The Eighth International Conference on Web Search and Data Mining. NY, United States of America : ACM, 2014. Ch. 58. P. 429-434.
- Chapter E. Morenko, Artemova E., Mirkin B. Conceptual maps: construction over a text collection and analysis, in: Analysis of Images, Social Networks and Texts Third International Conference, AIST 2014, Yekaterinburg, Russia, April 10-12, 2014, Revised Selected Papers / Ed. by D. I. Ignatov, M. Y. Khachay, A. Panchenko, N. Konstantinova, R. Yavorskiy. Vol. 439. Berlin : Springer, 2014. P. 163-169.
- Article Артемова Е. Л., Миркин Б. Г. Меры релевантности строка-текст в проблеме рубрикации научных статей // Бизнес-информатика. 2014. № 2. С. 51-62.
- Article Ильвовский Д. А., Артемова Е. Л. Системы автоматической обработки текстов // Открытые системы. СУБД. 2014. № 01. С. 51-53.
- Chapter Artemova E., Mirkin B. Computationally refining a taxonomy by using annotated suffix trees over Wikipedia resources, in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (Бекасово, 29 мая - 2 июня 2013 г.). В 2-х т. Т. 2: Доклады специальных секций. Вып. 12(19). М. : РГГУ, 2013. P. 177-185.
- Chapter Дубов М. С., Артемова Е. Л. Аннотированные суффиксные деревья: особенности реализации // В кн.: Доклады всероссийской научной конференции АИСТ’2013 / Отв. ред.: Артемова Е. Л.; науч. ред.: Д. И. Игнатов, М. Ю. Хачай, О. Баринова. М. : Национальный открытый университет «ИНТУИТ», 2013. С. 49-57.
- Book Доклады всероссийской научной конференции АИСТ’2013 / Отв. ред.: Артемова Е. Л.; науч. ред.: Д. И. Игнатов, М. Ю. Хачай, О. Баринова. М. : Национальный открытый университет «ИНТУИТ», 2013.
- Chapter Артемова Е. Л., Миркин Б. Г. Использование ресурсов Интернета для построения таксономии // В кн.: Доклады всероссийской научной конференции АИСТ’2013 / Отв. ред.: Артемова Е. Л.; науч. ред.: Д. И. Игнатов, М. Ю. Хачай, О. Баринова. М. : Национальный открытый университет «ИНТУИТ», 2013. С. 36-48.
- Chapter Ignatov D. I., Kaminskaya A. Y., Bezzubtseva A. A., Artemova E., Blinkin K. N., Nedumov D. R., Chugunova O. N., Konstantinov A. V., Romashkin N. S., Strok F. V., Goncharova D., Yavorskiy R. CrowDM: the System for Collaborative Platform Data Analysis, in: EEML 2012 – Experimental Economics in Machine Learning / Ed. by R. Tagiew, D. I. Ignatov, A. Neznanov, J. Poelmans. Leuven : Katholieke Universiteit Leuven, 2012. P. 61-71.
- Chapter Ignatov D. I., Kaminskaya A. Y., Bezzubtseva A. A., Artemova E., Blinkin K. N., Nedumov D. R., Chugunova O. N., Konstantinov A. V., Romashkin N. S., Strok F. V., Goncharova D., Poelmans J., Yavorskiy R. Mining Complex Data Generated by Collaborative Platforms, in: Перспективные направления исследований в области бизнес-информатики: Материалы XI международной конференции / Отв. ред.: N. Aseeva, O. Kozyrev.; Ed. by E. Babkin. Nizhny Novgorod : Higher School of Economics in Nizhny Novgorod, 2012. P. 7-17.
- Chapter Артемова Е. Л., Чугунова О. Н., Аскарова Ю. А., Насименто С., Миркин Б. Г. Автоматизация использования таксономий для аннотирования текстовых документов. // В кн.: Анализ изображений, сетей и текстов. Доклады всероссийской научной конференции АИСТ'12. Модели, алгоритмы и инструменты анализа данных; результаты и возможности для анализа изображений, сетей и текстов. Екатеринбург, 16 – 18 марта 2012 года / Науч. ред.: Д. И. Игнатов, Р. Э. Яворский. Вып. 1. М. : Национальный открытый университет «ИНТУИТ», 2012. С. 97-103.
- Article Миркин Б. Г., Артемова Е. Л., Чугунова О. Н. Метод аннотированного суффиксного дерева для оценки степени вхождения строк в текстовые документы // Бизнес-информатика. 2012. Т. 3. № 21. С. 31-41.
- Chapter Artemova E., Чугунова О. Н., Аскарова Ю. А., Nascimento S., Mirkin B. Abstracting concepts from text documents by using an ontology, in: CDUD – 2010: International Workshop on Concept Discovery in Unstructured Data. M. : Higher School of Economics Publishing House, 2011. P. 20-31.
What books to read in lockdown
Self-isolation is a perfect time for starting hobbies, improving personal skills, or just reading a new book. We could help with the last one. Our faculty members and administrative staff gave some recommendations on what to read during the self-isolation
‘Whatever I’m Researching, I Want to Be Able to Put It into Practice’
Md Tahsir Ahmed Munna is a second-year master’s student in Data Science at HSE. He is also one of the international students nominated for HSE’s Silver Nestling Award in 2019. Munna’s master’s thesis focuses on knowledge graph-based recommender systems, and he works as a research assistant at HSE’s Laboratory for Models and Methods of Computational Pragmatics. HSE News Service spoke with Munna about studying Data Science at HSE, working on his thesis, and living in Moscow.
Members of the Faculty Participated in Global Engineering Week at Chitkara University in India
In mid-October, staff members of the Faculty of Computer Science, Sergey Zykov and Ekaterina Chernyak, visited Chitkara University, one of the largest private universities in India. They conducted courses on software life cycles and automated text processing as part of the university’s annual school in engineering.
How Students of the Faculty Spend Their Summer
In July 2015, students of the Faculty of Computer Science finished their exams, but not their studies. This summer they will be having internships in leading IT companies, conducting research at HSE research departments and taking part in summer schools. Students of the faculty will also work as lecturers and assistants at some of the events.
Faculty of Computer Science to Offer Courses during HSE Summer University
Computer Science programme of the Summer University provides unique opportunities for students from around the world. The programme covers various topics in Computer Science from purely theoretical to applied and practical. Theoretical side of the programme includes both a detailed introduction to the theory of computations and more advanced topics in Artificial Intelligence and Statistical Diagnosis. Practical aspects of the programme are tightly integrated with theoretical material. Participants of the programme will have an opportunity to apply the new knowledge in their own programming experience, for example, in processing of natural languages, creating a distributed computing system or implementing a compiler for a programming language.
