Document Cohesion Flow: Striving towards Coherence

Scott A. Crossley, Mihai Dascalu, Stefan Trausan-Matu, Laura K. Allen, Danielle S. McNamara

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

5 Scopus citations

Abstract

Text cohesion is an important element of discourse processing. This paper presents a new approach to modeling, quantifying, and visualizing text cohesion using automated cohesion flow indices that capture semantic links among paragraphs. Cohesion flow is calculated by applying Cohesion Network Analysis, a combination of semantic distances, Latent Semantic Analysis, and Latent Dirichlet Allocation, as well as Social Network Analysis. Experiments performed on 315 timed essays indicated that cohesion flow indices are significantly correlated with human ratings of text coherence and essay quality. Visualizations of the global cohesion indices are also included to support a more facile understanding of how cohesion flow impacts coherence in terms of semantic dependencies between paragraphs.

Original languageEnglish (US)
Title of host publicationProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
EditorsAnna Papafragou, Daniel Grodner, Daniel Mirman, John C. Trueswell
PublisherThe Cognitive Science Society
Pages764-769
Number of pages6
ISBN (Electronic)9780991196739
StatePublished - 2016
Externally publishedYes
Event38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 - Philadelphia, United States
Duration: Aug 10 2016Aug 13 2016

Publication series

NameProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016

Conference

Conference38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016
Country/TerritoryUnited States
CityPhiladelphia
Period8/10/168/13/16

Keywords

  • Coherence
  • Cohesion Flow
  • Cohesion Network Analysis
  • Natural Language Processing, Computational Models
  • Writing Quality

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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