Name
Abstract | ||
|---|---|---|
In this paper, a new method for question classification is proposed, which employs ensemble learning algorithms MultiBoost to train multiple question classifiers. These component learners are combined to produce the final hypothesis. In detail, the feature spaces are obtained through extracting high-frequency keywords from questions corpus and the method of word semantic similarity is performed to adjust the feature weights. Then, the question classifiers are trained from this vector space. The ensemble method, MultiBoost, is applied to construct an ensemble of classifiers to tackle the problem of question classification. Experiments on the Chinese question system of tourism domain show that the ensemble methods could effectively improve the classification accuracy. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/FSKD.2011.6019826 | FSKD |
Keywords | Field | DocType |
ensemble learning,multiboost,question classification,word semantic similarity,machine learning,learning artificial intelligence,vector space,travel industry,feature space,text analysis,accuracy,semantics,bagging,classification algorithms,high frequency,feature extraction,semantic similarity | Semantic similarity,Vector space,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Statistical classification,Ensemble learning,Machine learning,Semantics,Word processing | Conference |
Volume | Issue | Citations |
3 | null | 0 |
PageRank | References | Authors |
0.34 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Su | 1 | 0 | 0.34 |
| Zhengtao Yu | 2 | 460 | 69.08 |
| Jianyi Guo | 3 | 20 | 10.99 |
| Cunli Mao | 4 | 51 | 11.54 |
| Yun Liao | 5 | 3 | 2.43 |