Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

Question Classification Based on Improved TFIDF Algorithm

Authors
Jing Gao, Cui-xiao Zhang, Zhiqiang Wang, Guangzhen Zhao, Xuan Li
Corresponding Author
Jing Gao
Available Online June 2017.
DOI
https://doi.org/10.2991/caai-17.2017.80How to use a DOI?
Keywords
TFIDF; mutual information; information entropy; space vector model; questioning classification
Abstract
The feature weight calculation is an important part of question classification, feature weights directly affect the accuracy of the classification results, the traditional TFIDF algorithm is widely applied in the field, but it ignores the relationship between features and classes. Therefore, this paper introduces information entropy and mutual information to improve the traditional TFIDF, I convert the computed weight results into a space vector model format, and use the support vector machine (SVM) for question classification. Finally, I use TFIDF, TFIDF-MI and TFIDF-MI-E these three methods to calculate the feature weight, and compare the results of the classification experiments. The experimental results show that the accuracy, recall and F value of TFIDF-MI-E algorithm are higher than those of TFIDF and TFIDF-MI two algorithm.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-360-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/caai-17.2017.80How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jing Gao
AU  - Cui-xiao Zhang
AU  - Zhiqiang Wang
AU  - Guangzhen Zhao
AU  - Xuan Li
PY  - 2017/06
DA  - 2017/06
TI  - Question Classification Based on Improved TFIDF Algorithm
BT  - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 354
EP  - 357
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.80
DO  - https://doi.org/10.2991/caai-17.2017.80
ID  - Gao2017/06
ER  -