Ensemble Learning on Scoring Student Essay

DOI

10.2991/mehss-18.2018.52How to use a DOI?

Keywords

Ensemble Learning, Word2vec, Nature Language Processing, Score Essay.

Abstract

Automated essay scoring is becoming more and more concerned by the researchers. In this work, we develop a new way to extract Textual features, which is proved to be valid. First, we calculate the Distributed Representation from the WiKi corpus by the word2vec. Then we calculate the number of words, the number of dictionary,the diversity of words as the textual features by K-means and Distributed Representation. There will be 3*k textual features as the k represents the number of categories. Besides, we calculate the structure features including the length of essay,the number of paragraph, the length of sentence etc. We use several models such as XGBoost, Random Forest, GBDT to train the training set and predict the test set.Finally, We ensemble the prediction of those models as the final prediction.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haokun Liu
AU  - Yan Ye
AU  - Min Wu
PY  - 2018/04
DA  - 2018/04
TI  - Ensemble Learning on Scoring Student Essay
BT  - Proceedings of the 2018 International Conference on Management and Education, Humanities and Social Sciences (MEHSS 2018)
PB  - Atlantis Press
SP  - 250
EP  - 255
SN  - 2352-5398
UR  - https://doi.org/10.2991/mehss-18.2018.52
DO  - 10.2991/mehss-18.2018.52
ID  - Liu2018/04
ER  -