Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/59640
Type of publication: Straipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a)
Field of Science: Filologija / Philology (H004)
Author(s): Kalinauskaitė, Danguolė
Title: Detecting information-dense texts: towards an automated analysis
Is part of: CEUR Workshop proceedings [electronic resource]: IVUS 2018, International conference on information technologies, Kaunas, Lithuania, 27 April, 2018. Aachen : CEUR-WS, 2018, Vol. 2145
Extent: p. 95-98
Date: 2018
Keywords: Lexical density;Information density;Latural language processing;Computational linguistics
Abstract: Determining information density has become a central issue in natural language processing. While information density is seen as too complex to measure globally, a study of lexical and syntactic features allows a comparison of information density between different texts or different text genres. This paper provides a part of methodology proposed for automatic analysis of information density based on lexical and syntactic levels of language
Internet: http://ceur-ws.org/Vol-2145/p17.pdf
http://ceur-ws.org/Vol-2145/p17.pdf
Affiliation(s): Baltijos pažangių technologijų institutas
Humanitarinių mokslų fakultetas
Lituanistikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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