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Improving neural network models for natural language processing in Russian with synonyms

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dc.contributor.author Galinsky R.
dc.contributor.author Alekseev A.
dc.contributor.author Nikolenko S.
dc.date.accessioned 2018-09-19T22:36:54Z
dc.date.available 2018-09-19T22:36:54Z
dc.date.issued 2017
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/145333
dc.description.abstract © 2016 FRUCT.Recent advances in deep leaming for natural language processing achieve and improve over state of the art results in many natural language processing tasks. One problem with neural network models, however, is that they require large datasets, including large labeled datasets for the corresponding problems. In this work, we suggest a dala augmentation method based on extending a given dataset with synonyms for the words appearing there. We apply this approach to the morphologically rich Russian language and show improvements for modem neural network NLP models on standard tasks such as sentiment analysis.
dc.title Improving neural network models for natural language processing in Russian with synonyms
dc.type Conference Paper
dc.collection Публикации сотрудников КФУ
dc.source.id SCOPUS-2017-SID85018414276


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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