Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
---|---|---|
Article Number | 02020 | |
Number of page(s) | 4 | |
Section | 3D Images Reconstruction and Virtual System | |
DOI | https://doi.org/10.1051/matecconf/201823202020 | |
Published online | 19 November 2018 |
RST-based Discourse Coherence Quality Analysis Model for Students’ English Essays
School of Information and Communication Engineering, Guilin University of Electronic Technology, Guilin, China
a Corresponding author: 1098382665@qq.com
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.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.