Evaluating Children's Composition Based on Chinese Linguistic Features with Machine Learning

Yangjun Chen, Calvin C.Y. Liao, Sannyuya Liu, Hercy N.H. Cheng, Liansheng Jia, Jianwen Sun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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%.

Original languageEnglish
Title of host publicationProceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
EditorsKiyota Hashimoto, Naoki Fukuta, Tokuro Matsuo, Sachio Hirokawa, Masao Mori, Masao Mori
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages729-734
Number of pages6
ISBN (Electronic)9781538606216
DOIs
StatePublished - 15 Nov 2017
Event6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 - Hamamatsu, Shizuoka, Japan
Duration: 9 Jul 2017 → …

Publication series

NameProceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017

Conference

Conference6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
Country/TerritoryJapan
CityHamamatsu, Shizuoka
Period9/07/17 → …

Keywords

  • Linguistic feature
  • Pupils' Chinese compositions
  • Stepwise multiple linear regression
  • Support Vector Machine

Fingerprint

Dive into the research topics of 'Evaluating Children's Composition Based on Chinese Linguistic Features with Machine Learning'. Together they form a unique fingerprint.

Cite this