Question classification (QC) is a prime constituent of automated question answering system. The work presented here demonstrates that the combination of multiple models achieve better classification performance than those obtained with existing individual models for the question classification task in Bengali. We have exploited state-of-the-art multiple model combination techniques, i.e., ensemble, stacking and voting, to increase QC accuracy. Lexical, syntactic and semantic features of Bengali questions are used for four well-known classifiers, namely Na\"{\i}ve Bayes, kernel Na\"{\i}ve Bayes, Rule Induction, and Decision Tree, which serve as our base learners. Single-layer question-class taxonomy with 8 coarse-grained classes is extended to two-layer taxonomy by adding 69 fine-grained classes. We carried out the experiments both on single-layer and two-layer taxonomies. Experimental results confirmed that classifier combination approaches outperform single classifier classification approaches by 4.02 %DIFDELCMD < \\%%% %DIF < for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79\\% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.abstract 
Question classification (QC) is a prime constituent of automated question answering system. The work presented here demonstrates that the combination of multiple models achieve better classification performance than those obtained with existing individual models for the question classification task in Bengali. We have exploited state-of-the-art multiple model combination techniques, i.e., ensemble, stacking and voting, to increase QC accuracy. Lexical, syntactic and semantic features of Bengali questions are used for four well-known classifiers, namely Na\"{\i}ve Bayes, kernel Na\"{\i}ve Bayes, Rule Induction, and Decision Tree, which serve as our base learners. Single-layer question-class taxonomy with 8 coarse-grained classes is extended to two-layer taxonomy by adding 69 fine-grained classes. We carried out the experiments both on single-layer and two-layer taxonomies. Experimental results confirmed that classifier combination approaches outperform single classifier classification approaches by 4.02 %DIFDELCMD < \\%%% %DIF < for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79\\% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.abstract \% for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79\% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.
A
\% for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79\% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.