Thank "Goodness"! A Way to Measure Style in Student Essays
被引:0
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作者:
Mathias, Sandeep
论文数: 0引用数: 0
h-index: 0
机构:
Indian Inst Technol, Dept Comp Sci & Engn, Ctr Indian Language Technol, Bombay, Maharashtra, IndiaIndian Inst Technol, Dept Comp Sci & Engn, Ctr Indian Language Technol, Bombay, Maharashtra, India
Mathias, Sandeep
[1
]
Bhattacharyya, Pushpak
论文数: 0引用数: 0
h-index: 0
机构:
Indian Inst Technol, Dept Comp Sci & Engn, Ctr Indian Language Technol, Bombay, Maharashtra, IndiaIndian Inst Technol, Dept Comp Sci & Engn, Ctr Indian Language Technol, Bombay, Maharashtra, India
Bhattacharyya, Pushpak
[1
]
机构:
[1] Indian Inst Technol, Dept Comp Sci & Engn, Ctr Indian Language Technol, Bombay, Maharashtra, India
来源:
NATURAL LANGUAGE PROCESSING TECHNIQUES FOR EDUCATIONAL APPLICATIONS
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2018年
关键词:
D O I:
暂无
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
Essays have two major components for scoring - content and style. In this paper, we describe a property of the essay, called goodness, and use it to predict the score given for the style of student essays. We compare our approach to solve this problem with baseline approaches, such as language modeling and also a state-of-the-art deep learning system, proposed by Taghipour and Ng (2016). We show that, despite being quite intuitive, our approach is very powerful in predicting the style of the essays.