Участник:MEremeev
Материал из MachineLearning.
Maksim Eremeev
Senior BSc. student of Lomonosov Moscow State University, faculty of Computational Mathematics and Cybernetics, Mathematical Methods of Forecasting department.
Scientific Advisor: Konstantin Vorontsov
SCOPUS ID = 57212339802
ORCID = 0000-0001-7459-7290
Research Interests
Majoring in Machine Learning and Data Analysis, my primary research interests are Natural Language Processing, Topic Modeling and Information Search.
Currently, I conduct research on automatic estimation of text complexity and generating reading orders. If succeeded, the novel algorithms in ranking exploratory search results will be introduced.
Conference Talks
| Date | Event | Topic | Materials |
|---|---|---|---|
| 20.02.2020 | Poster Session at OpenTalks.AI conference | Automatically estimating text complexity | Poster |
| 29.11.2019 | Talk at Mathematical Methods of Pattern Recognition conference (MMPR) | Quantile-based approach to estimating cognitive complexity | Slides |
| 04.09.2019 | Poster Session at Recent Advances of Natural Language Processing conference (RANLP) | Lexical Quantile-based Text Complexity Measure | Poster |
| 11.05.2019 | Talk at ODS DataFest conference, Science Day | Estimating text complexity to rank exploratory search results | Slides |
| 10.04.2019 | National Students conference Lomonosov, Mathematical Methods of Forecasting section | Exploratory search based on Topic Modeling | Slides |
Publications
- M.Eremeev, A.Yanina. 2019. Exploratory Search based on Topic Modelling (in Russian). Book of Abstracts of XXVI International Conference of Students, Graduates and Young Scientists “Lomonosov-2019”, Computational Mathematics and Cybernetics section, Moscow, Russia, 2019. Text
- M.Eremeev, K.Vorontsov. 2019. Lexical Quantile-Based Text Complexity Measure. In Proceedings of the 12th International Conference on “Recent Advances in Natural Language Processing” (Scopus-indexed), Varna, Bulgaria, 2019. Text
- M.Eremeev, K.Vorontsov. 2019. Quantile-based approach of measuring cognitive complexity of text. In proceedings of Russian National Conference MMPR-2019, Moscow, Russia, 2019. Text
