Question Classification Based on Improved TFIDF Algorithm
Jing Gao, Cui-xiao Zhang, Zhiqiang Wang, Guangzhen Zhao, Xuan Li
Available Online June 2017.
- https://doi.org/10.2991/caai-17.2017.80How to use a DOI?
- TFIDF; mutual information; information entropy; space vector model; questioning classification
- 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.
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 -