1 code implementation • LREC 2022 • Elena Mikhalkova, Alexander A. Khlyupin
In this article we describe a Jeopardy!-like Russian QA data set collected from the official Russian quiz database Ch-g-k.
1 code implementation • 4 Dec 2021 • Elena Mikhalkova
Question answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields.
no code implementations • 14 Nov 2021 • Elena Mikhalkova, Timofei Protasov, Anastasiia Drozdova, Anastasiia Bashmakova, Polina Gavin
Literary texts are usually rich in meanings and their interpretation complicates corpus studies and automatic processing.
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 • LREC 2020 • Elena Mikhalkova, Timofei Protasov, Polina Sokolova, Anastasiia Bashmakova, Anastasiia Drozdova
Then we corrected the guidelines and added computer annotation of verb forms with the purpose to get a higher raters{'} agreement and tested them again on the short story {``}The Gift of the Magi{''} by O. Henry.
no code implementations • SEMEVAL 2018 • Elena Mikhalkova, Yuri Karyakin, Alex Voronov, er, Dmitry Grigoriev, Artem Leoznov
The paper describes our search for a universal algorithm of detecting intentional lexical ambiguity in different forms of creative language.
no code implementations • 18 Jul 2017 • Elena Mikhalkova, Yuri Karyakin
The article describes a model of automatic analysis of puns, where a word is intentionally used in two meanings at the same time (the target word).
1 code implementation • SEMEVAL 2017 • Elena Mikhalkova, Yuri Karyakin
The article describes a model of automatic interpretation of English puns, based on Roget's Thesaurus, and its implementation, PunFields.
no code implementations • 18 Jul 2017 • Elena Mikhalkova, Nadezhda Ganzherli, Yuri Karyakin
Being a matter of cognition, user interests should be apt to classification independent of the language of users, social network and content of interest itself.