| Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
|---|---|---|
| Article Number | 01035 | |
| Number of page(s) | 5 | |
| Section | Network Security System, Neural Network and Data Information | |
| DOI | https://doi.org/10.1051/matecconf/201823201035 | |
| Published online | 19 November 2018 | |
Off-topic English Essay Detection Model Based on Hybrid Semantic Space for Automated English Essay Scoring System
School of Information and Communication Engineering, Guilin University of Electronic Technology, Guilin, China
a Corresponding author: 239717061@qq.com
Aiming at the problem that the lack of accurate and efficient off-topic detection model for current Automated English Scoring System in China, an unsupervised off-topic essay detection model based on hybrid semantic space was proposed. Firstly, the essay and its essay prompt are respectively represented as noun phrases by using a neural-network dependency parser. Secondly, we introduce a method to construct a hybrid semantic space. Thirdly, we propose a method to represent the noun phrases of the essay and its prompt as vectors in hybrid semantic space and calculate the similarity between the essay and its prompt by using the noun phrase vectors of them. Finally, we propose a sort method to set the off-topic threshold so that the off-topic essays can be identified efficiently. The experimental results on four datasets totaling 5000 essays show that, compared to the previous off-topic essay detection models, the proposed model can detect off-topic essays with higher accuracy, and the accuracy rate over all essay data sets reaches 89.8%.
© 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.