Discriminating between second language learning text-types

Scott A. Crossley, Philip M. McCarthy, Danielle S. McNamara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Text classification remains one of the major fields of research in natural language processing. This paper evaluates the use of the computational tool Coh-Metrix as a means to distinguish between seemingly similar text-types. Using a discriminant analysis on a corpus of second language reading texts, this paper demonstrates that Coh-Metrix is able to significantly distinguish authentic text-types from ones that have been specifically simplified for second language readers. This paper offers important findings for text classification research and for second language reading materials developers and second language teachers by demonstrating that moderate, shallow, textual changes can affect discourse structures.

Original languageEnglish (US)
Title of host publicationProceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007
Pages205-210
Number of pages6
StatePublished - Dec 28 2007
Externally publishedYes
Event20th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007 - Key West, FL, United States
Duration: May 7 2007May 9 2007

Publication series

NameProceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007

Other

Other20th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007
CountryUnited States
CityKey West, FL
Period5/7/075/9/07

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Fingerprint Dive into the research topics of 'Discriminating between second language learning text-types'. Together they form a unique fingerprint.

Cite this