Detecting information-dense texts: towards an automated analysis

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

MARC21 XML metadata

Show full item record

132

checked on Nov 2, 2019

10

checked on Nov 2, 2019

Check