• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Wang, Zining (Wang, Zining.) | Liu, Jianli (Liu, Jianli.) | Dong, Ruihai (Dong, Ruihai.)

收录:

EI Scopus

摘要:

Teachers tend to set the free-text questions for testing the comprehensive ability of students. That leads to the increasing attention to the intelligent auto-grading system for easing the grading load on examiners. In this paper, we present a novel automatic essay scoring system based on Natural Language Processing and Deep Learning technologies. In particular, the proposed system encodes an essay as sequential embeddings and harnesses a bi-directional LSTM to catch the semantic information. Meanwhile, the system constructs the attention for each essay so that the network can learn to focus on the valid information correctly in an article, which can also provide the reasonable evidence of the predictive result. The dataset for training and testing is the public essay set available in the Automated Student Assessment Prize on Kaggle. The study shows that our system achieves state-of-the-art performance in grade prediction, and more importantly, our intelligent auto-grading system can focus on the critical words and sentences, analyze the logical semantic relationship of the context and predict the interpretable grades. © 2018 IEEE.

关键词:

Ability testing Cloud computing Deep learning Deep neural networks Grading Learning systems Long short-term memory Natural language processing systems Neural networks Semantics Statistical tests

作者机构:

  • [ 1 ] [Wang, Zining]Beijing Dublin International College, Beijing University of Technology, China
  • [ 2 ] [Liu, Jianli]Beijing Dublin International College, Beijing University of Technology, China
  • [ 3 ] [Dong, Ruihai]Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

相关文章:

来源 :

年份: 2019

页码: 430-435

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 18

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 2

在线人数/总访问数:1006/3595768
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司