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Information Journal Paper

Title

QUESTION CLASSIFICATION USING ENSEMBLE CLASSIFIERS

Pages

  99-112

Abstract

 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.

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    APA: Copy

    GHAEMI, HADI, & KAHANI, MOHSEN. (2016). QUESTION CLASSIFICATION USING ENSEMBLE CLASSIFIERS. SIGNAL AND DATA PROCESSING, 13(3 (SERIAL 29) ), 99-112. SID. https://sid.ir/paper/160904/en

    Vancouver: Copy

    GHAEMI HADI, KAHANI MOHSEN. QUESTION CLASSIFICATION USING ENSEMBLE CLASSIFIERS. SIGNAL AND DATA PROCESSING[Internet]. 2016;13(3 (SERIAL 29) ):99-112. Available from: https://sid.ir/paper/160904/en

    IEEE: Copy

    HADI GHAEMI, and MOHSEN KAHANI, “QUESTION CLASSIFICATION USING ENSEMBLE CLASSIFIERS,” SIGNAL AND DATA PROCESSING, vol. 13, no. 3 (SERIAL 29) , pp. 99–112, 2016, [Online]. Available: https://sid.ir/paper/160904/en

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