TY - GEN
T1 - Document Cohesion Flow
T2 - 38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016
AU - Crossley, Scott A.
AU - Dascalu, Mihai
AU - Trausan-Matu, Stefan
AU - Allen, Laura K.
AU - McNamara, Danielle S.
N1 - Funding Information:
This research was partially supported by the EC H2020 project RAGE Grant agreement No 644187, as well as by the Institute for Education Sciences (IES R305A080589 and IES R305G20018-02) and NSF (1417997 and 1418378).
Publisher Copyright:
© 2016 Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Coherence
KW - Cohesion Flow
KW - Cohesion Network Analysis
KW - Natural Language Processing, Computational Models
KW - Writing Quality
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M3 - Conference contribution
AN - SCOPUS:84996842422
T3 - Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
SP - 764
EP - 769
BT - Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
A2 - Papafragou, Anna
A2 - Grodner, Daniel
A2 - Mirman, Daniel
A2 - Trueswell, John C.
PB - The Cognitive Science Society
Y2 - 10 August 2016 through 13 August 2016
ER -