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Language-Independent Text Classifier Base on Recurrent Neural Networks

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU132446" target="_blank" >RIV/00216305:26220/19:PU132446 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Language-Independent Text Classifier Base on Recurrent Neural Networks

  • Original language description

    This paper deals with a proposal of language independent text classifiers based on recurrent neural networks. They work at a character level thus they do not require any text preprocessing. The classifiers have been trained and evaluated on a multilingual data set that is privately collected from film review databases. It contains Czech (Slovak), English, German and Spanish language subset. The resulting accuracy of the proposed language independent classifiers base on the recurrent neural networks in polarity sentiment analysis task is 78.55%.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 25th Conference STUDENT EEICT 2019

  • ISBN

    978-80-214-5735-5

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    754-758

  • Publisher name

    Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

  • Place of publication

    Brno

  • Event location

    Brno

  • Event date

    Apr 25, 2019

  • Type of event by nationality

    CST - Celostátní akce

  • UT code for WoS article