Document Cohesion Flow: Striving towards Coherence

Scott Crossly; Mihai Dascalu; Stefan Trausan-Matu; Laura Allen; Danielle S. McNamara

Document Cohesion Flow: Striving towards Coherence

Scott Crossly, Mihai Dascalu, Stefan Trausan-Matu, Laura Allen, Danielle S. McNamara

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

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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 language	English
Title of host publication	CogSci 2016 Proceedings
Editors	A. Papafragou, D. Grodner, D. Mirman, J.C. Trueswell
Place of Publication	Austin, TX
Publisher	Cognitive Science Society
Pages	764-769
ISBN (Print)	978-0-9911967-3-9
Publication status	Published - Aug 2016
Externally published	Yes
Event	38th Annual Conference of the Cognitive Science Society: Recognizing and Representing Events - Philadelphia, United States Duration: 10 Aug 2016 → 13 Aug 2016 https://mindmodeling.org/cogsci2016/

Conference

Conference	38th Annual Conference of the Cognitive Science Society
Abbreviated title	COGSCI2016
Country/Territory	United States
City	Philadelphia
Period	10/08/16 → 13/08/16
Internet address	https://mindmodeling.org/cogsci2016/

Keywords

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

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paper0142Final published version, 353 KBLicence: CC BY-NC-ND

Rage: Realising an Applied Gaming Eco-system
Westera, W. (PI), Georgiadis, K. (CoI), Saveski, G. (CoI), van Lankveld, G. (CoI), Bahreini, K. (CoI), van der Vegt, W. (CoI), Berkhout, J. (CoI), Nyamsuren, E. (CoI), Kluijfhout, E. (CoI) & Nadolski, R. (CoI)
European Union
1/02/15 → 31/07/19
Project: Research

Cite this

@inproceedings{39d2820f41f449ba9c58a3ff5850880d,

title = "Document Cohesion Flow: Striving towards Coherence",

abstract = "Text cohesion is an important element of discourseprocessing. This paper presents a new approach to modeling,quantifying, and visualizing text cohesion using automatedcohesion flow indices that capture semantic links amongparagraphs. Cohesion flow is calculated by applyingCohesion Network Analysis, a combination of semanticdistances, Latent Semantic Analysis, and Latent DirichletAllocation, as well as Social Network Analysis. Experimentsperformed on 315 timed essays indicated that cohesion flowindices are significantly correlated with human ratings of textcoherence and essay quality. Visualizations of the globalcohesion indices are also included to support a more facileunderstanding of how cohesion flow impacts coherence interms of semantic dependencies between paragraphs.",

keywords = "Cohesion Flow, Natural Language Processing, Computational Models, Cohesion Network Analysis, Coherence, Writing Quality",

author = "Scott Crossly and Mihai Dascalu and Stefan Trausan-Matu and Laura Allen and McNamara, {Danielle S.}",

year = "2016",

month = aug,

language = "English",

isbn = "978-0-9911967-3-9",

pages = "764--769",

editor = "A. Papafragou and D. Grodner and D. Mirman and J.C. Trueswell",

booktitle = "CogSci 2016 Proceedings",

publisher = "Cognitive Science Society",

address = "United States",

note = "38th Annual Conference of the Cognitive Science Society : Recognizing and Representing Events, COGSCI2016 ; Conference date: 10-08-2016 Through 13-08-2016",

url = "https://mindmodeling.org/cogsci2016/",

}

Document Cohesion Flow: Striving towards Coherence. / Crossly, Scott; Dascalu, Mihai; Trausan-Matu, Stefan et al.
CogSci 2016 Proceedings. ed. / A. Papafragou; D. Grodner; D. Mirman; J.C. Trueswell. Austin, TX: Cognitive Science Society, 2016. p. 764-769.

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

TY - GEN

T1 - Document Cohesion Flow: Striving towards Coherence

AU - Crossly, Scott

AU - Dascalu, Mihai

AU - Trausan-Matu, Stefan

AU - Allen, Laura

AU - McNamara, Danielle S.

PY - 2016/8

Y1 - 2016/8

N2 - Text cohesion is an important element of discourseprocessing. This paper presents a new approach to modeling,quantifying, and visualizing text cohesion using automatedcohesion flow indices that capture semantic links amongparagraphs. Cohesion flow is calculated by applyingCohesion Network Analysis, a combination of semanticdistances, Latent Semantic Analysis, and Latent DirichletAllocation, as well as Social Network Analysis. Experimentsperformed on 315 timed essays indicated that cohesion flowindices are significantly correlated with human ratings of textcoherence and essay quality. Visualizations of the globalcohesion indices are also included to support a more facileunderstanding of how cohesion flow impacts coherence interms of semantic dependencies between paragraphs.

AB - Text cohesion is an important element of discourseprocessing. This paper presents a new approach to modeling,quantifying, and visualizing text cohesion using automatedcohesion flow indices that capture semantic links amongparagraphs. Cohesion flow is calculated by applyingCohesion Network Analysis, a combination of semanticdistances, Latent Semantic Analysis, and Latent DirichletAllocation, as well as Social Network Analysis. Experimentsperformed on 315 timed essays indicated that cohesion flowindices are significantly correlated with human ratings of textcoherence and essay quality. Visualizations of the globalcohesion indices are also included to support a more facileunderstanding of how cohesion flow impacts coherence interms of semantic dependencies between paragraphs.

KW - Cohesion Flow

KW - Natural Language Processing

KW - Computational Models

KW - Cohesion Network Analysis

KW - Coherence

KW - Writing Quality

M3 - Conference Article in proceeding

SN - 978-0-9911967-3-9

SP - 764

EP - 769

BT - CogSci 2016 Proceedings

A2 - Papafragou, A.

A2 - Grodner, D.

A2 - Mirman, D.

A2 - Trueswell, J.C.

PB - Cognitive Science Society

CY - Austin, TX

T2 - 38th Annual Conference of the Cognitive Science Society

Y2 - 10 August 2016 through 13 August 2016

ER -

Document Cohesion Flow: Striving towards Coherence

Abstract

Conference

Keywords

Access to Document

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Rage: Realising an Applied Gaming Eco-system

Cite this