RST-based Discourse Coherence Quality Analysis Model for Students’ English Essays

Against the problems which can’t be solved by the word-level based local coherence analysis model, we propose a new discourse coherence quality analysis model (abbreviated RST-DCQA) by analyzing the full hierarchical discourse structure of English essays. Under the framework of rhetorical structure...

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Bibliographic Details
Main Authors: Huang Guimin, Tan Min, Sun Zhenglin, Zhou Ya
Format: Article
Language:English
Published: EDP Sciences 2018-01-01
Series:MATEC Web of Conferences
Online Access:https://doi.org/10.1051/matecconf/201823202020
Description
Summary:Against the problems which can’t be solved by the word-level based local coherence analysis model, we propose a new discourse coherence quality analysis model (abbreviated RST-DCQA) by analyzing the full hierarchical discourse structure of English essays. Under the framework of rhetorical structure theory (RST), firstly, we design an RST-style discourse relations parser to capture the deep hierarchical discourse structure of essays; secondly, we transform the discourse relation information into a discourse relation matrix; finally, we design an algorithm to analyze the discourse coherence quality of student’s English essays. The experimental results show that the average error of our model’s score and teacher’s score is only 2.63, and the Pearson correlation coefficient is 0.71. Compared with the other models, our RST-DCQA model has a higher accuracy and better practicality in the field of students’ essays assessment.
ISSN:2261-236X