@inproceedings{ef5b023384574d1d8327eac3d6c7ba0f,
title = "Evaluating Children's Composition Based on Chinese Linguistic Features with Machine Learning",
abstract = "The traditional evaluation of composition is human evaluation which is time-consuming, laborious and easily affected by subjective. In recent years, the automatic essay scoring (AES) has become a hot issue in natural language processing, but few research focus on Chinese AES. Hence, this study designed a Chinese AES system and collected 4566 compositions from first grade to sixth grade students. We also extracted 43 linguistic features based on Chinese characteristic, and analysis these compositions based on three model by stepwise multiple regression technique and support vector machine. Results showed that the accuracy of classification is among 70~80%.",
keywords = "Linguistic feature, Pupils' Chinese compositions, Stepwise multiple linear regression, Support Vector Machine",
author = "Yangjun Chen and Liao, {Calvin C.Y.} and Sannyuya Liu and Cheng, {Hercy N.H.} and Liansheng Jia and Jianwen Sun",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 ; Conference date: 09-07-2017",
year = "2017",
month = nov,
day = "15",
doi = "10.1109/IIAI-AAI.2017.47",
language = "???core.languages.en_GB???",
series = "Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "729--734",
editor = "Kiyota Hashimoto and Naoki Fukuta and Tokuro Matsuo and Sachio Hirokawa and Masao Mori and Masao Mori",
booktitle = "Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017",
}