no code implementations • 17 Dec 2023 • Anna Glazkova, Dmitry Morozov
In our experiments, abstractive text summarization models fine-tuned for keyphrase generation show quite high results for a target text corpus.
no code implementations • 1 Nov 2023 • Anna Glazkova
The paper describes a system developed for Task 1 at SMM4H 2023.
1 code implementation • 8 Apr 2023 • Anna Glazkova
Our solution ranked 4th on the leaderboard while achieving an overall Pearson's r of 0. 599 over the test set.
1 code implementation • sdp (COLING) 2022 • Anna Glazkova, Maksim Glazkov
The paper describes neural models developed for the DAGPap22 shared task hosted at the Third Workshop on Scholarly Document Processing.
no code implementations • 8 Sep 2022 • Anna Glazkova, Dmitry Morozov
In this paper, we experiment with popular transformer-based models for abstractive text summarization using four benchmark datasets for keyphrase extraction.
1 code implementation • 25 Oct 2021 • Anna Glazkova, Michael Kadantsev, Maksim Glazkov
Our team called neuro-utmn-thales participated in two tasks on binary and fine-grained classification of English tweets that contain hate, offensive, and profane content (English Subtasks A & B) and one task on identification of problematic content in Marathi (Marathi Subtask A).
1 code implementation • SEMEVAL 2021 • Mikhail Kotyushev, Anna Glazkova, Dmitry Morozov
This paper describes our system for SemEval-2021 Task 5 on Toxic Spans Detection.
1 code implementation • 22 Dec 2020 • Anna Glazkova, Maksim Glazkov, Timofey Trifonov
In this paper, we present our results at the Constraint@AAAI2021 Shared Task: COVID-19 Fake News Detection in English.
1 code implementation • 24 Sep 2020 • Anna Glazkova, Yury Egorov, Maksim Glazkov
The ability to automatically determine the age audience of a novel provides many opportunities for the development of information retrieval tools.
1 code implementation • SEMEVAL 2020 • Elena Mikhalkova, Nadezhda Ganzherli, Anna Glazkova, Yuliya Bidulya
The article describes a fast solution to propaganda detection at SemEval-2020 Task 11, based onfeature adjustment.
no code implementations • 11 Aug 2020 • Anna Glazkova
In the paper, the authors compared the basic SMOTE method with its two modifications (Borderline SMOTE and ADASYN) and random oversampling technique on the example of one of text classification tasks.