  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% 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.
