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Modelling of word usage frequency dynamics using artificial neural network

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dc.contributor.author Maslennikova Y.
dc.contributor.author Bochkarev V.
dc.contributor.author Voloskov D.
dc.date.accessioned 2018-09-18T20:25:03Z
dc.date.available 2018-09-18T20:25:03Z
dc.date.issued 2014
dc.identifier.issn 1742-6588
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/139670
dc.description.abstract In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models. © Published under licence by IOP Publishing Ltd.
dc.relation.ispartofseries Journal of Physics: Conference Series
dc.title Modelling of word usage frequency dynamics using artificial neural network
dc.type Conference Paper
dc.relation.ispartofseries-issue 1
dc.relation.ispartofseries-volume 490
dc.collection Публикации сотрудников КФУ
dc.source.id SCOPUS17426588-2014-490-1-SID84896975330


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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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