Question Classification using Ensemble Classifiers

Question answering systems are produced and developed with the goal of providing short precise answers to questions asked in a natural language. Question classification is one the most important tasks in question answering systems. Question Classification is forecasting the type of response to given question in a natural language. Proposed methods divided into two categories: rule-based and machine learning-based. In this paper, a novel hybrid method for question classification was presented. The results of classifiers Combined By Voting, Behavior knowledge space, Naive Bayes, Decision Template and Dempster-Shafer Combination. The ensemble classifier exploits one rule-based classifier and two learning-based ones (SVM, Sparse Representation). The rule-based classifier includes a set of rules developed in the form of regular expressions. The learning-based classifications are based on lexical and syntactic features of questions. In the last part, the results of classification Combined By common methods in combination of one-class classifiers.