no code implementations • WS 2017 • Maria Ponomareva, Kirill Milintsevich, Ekaterina Chernyak, Anatoly Starostin
In this study we address the problem of automated word stress detection in Russian using character level models and no part-speech-taggers.
no code implementations • WS 2019 • Ekaterina Chernyak, Maria Ponomareva, Kirill Milintsevich
We explore how well a sequence labeling approach, namely, recurrent neural network, is suited for the task of resource-poor and POS tagging free word stress detection in the Russian, Ukranian, Belarusian languages.
no code implementations • 7 Mar 2019 • Gerhard Wohlgenannt, Ariadna Barinova, Dmitry Ilvovsky, Ekaterina Chernyak
Among the contributions are the evaluation of various word embedding techniques on the different task types, with the findings that even embeddings trained on small corpora perform well for example on the word intrusion task.
no code implementations • 4 Mar 2019 • Gerhard Wohlgenannt, Ekaterina Chernyak, Dmitry Ilvovsky, Ariadna Barinova, Dmitry Mouromtsev
In this research, we manually create high-quality datasets in the digital humanities domain for the evaluation of language models, specifically word embedding models.
no code implementations • WS 2017 • Ekaterina Chernyak
In this paper we address the problem of filtering obscene lexis in Russian texts.
no code implementations • WS 2016 • Gerhard Wohlgenannt, Ekaterina Chernyak, Dmitry Ilvovsky
In this paper a social network is extracted from a literary text.