Volume 13, Issue 3 (12-2016)                   JSDP 2016, 13(3): 99-112 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

ghaemi H, kahani M. Question Classification using Ensemble Classifiers . JSDP. 2016; 13 (3) :99-112
URL: http://jsdp.rcisp.ac.ir/article-1-270-en.html
master Ferdowsi University of Mashhad (FUM)
Abstract:   (797 Views)

Question answering systems are produced and developed to provide exact answers to the question posted in natural language. One of the most important parts of question answering systems is question classification. The purpose of question classification is predicting the kind of answer needed for the question in natural language. The  literature works can be categorized as rule-based and learning-based methods. This paper proposes a novel architecture for hybrid classification of questions. The results of the classifiers were combined by five methods of Weighted Voting, Behavior Knowledge space, Naive Bayes, Decision Template and Dempster-Shafer. The method uses a combination of two classifiers based on machine learning (Support Vector Machine and Sparse Representation) and one rule-based classifier. The learning-based classification uses the set of features extracted from the questions. The features are extracted on the basis of the lexical and syntactic structure of the questions. The results from the classifiers were combined by the methods that are common in the combination of one-class classifiers and the Obtained results indicate the improvement of the classification operations in comparison with the present methods. 

Full-Text [PDF 2923 kb]   (312 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2014/09/15 | Accepted: 2016/09/7 | Published: 2017/04/23 | ePublished: 2017/04/23

Add your comments about this article : Your username or Email:
Write the security code in the box

Send email to the article author

© 2015 All Rights Reserved | Signal and Data Processing