Automated detection of local coherence in short argumentative essays based on centering theory

Authors:
Vasile Rus;Nobal Niraula
Affiliations:
Department of Computer Science, The University of Memphis, Memphis, TN;Department of Computer Science, The University of Memphis, Memphis, TN
Venue:
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Year:
2012

Citing 8
Cited 0

Attention, intentions, and the structure of discourse

Computational Linguistics
Centering: a framework for modeling the local coherence of discourse

Computational Linguistics
Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays

IEEE Intelligent Systems
Discourse segmentation by human and automated means

Computational Linguistics
Evaluation of text coherence for electronic essay scoring systems

Natural Language Engineering
Automated scoring using a hybrid feature identification technique

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Representing Discourse Coherence: A Corpus-Based Study

Computational Linguistics
Speech and Language Processing (2nd Edition)

Speech and Language Processing (2nd Edition)

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe in this paper an automated method for assessing local coherence in short argumentative essays. We use ideas from Centering Theory to measure local coherence of essays' paragraphs and compare it to human judgments on one analytical feature of essay quality called Continuity. Paragraphs which correspond to a discourse segment in our work and which are dominated by one prominent concept were deemed locally coherent according to Centering Theory. A dominance measure was proposed based on which local coherence was judged. Results on a corpus of 184 argumentative essays showed promising results. Our findings also suggest that focusing on nominal subject for detecting candidate concepts for a discourse segment's central concept is sufficient, which confirms previous findings. Compared to previous approaches to assessing local discourse coherence in essays, our method is fully automated.