Semantic Based Automatic Question Generation using Artificial Immune System

Abstract

This research introduces a semantic based Automatic Question Generation System using both Semantic Role Labeling and Named Entity Recognition techniques to convert the input sentence into a semantic pattern. A training phase applied to build a classifier using an Artificial Immune System that will be able to classify the patterns according to the question type. The question types considered here are set of WH-questions like who, when, where, why, and how. Then a pattern matching phase is applied to select the best matching question pattern for the test sentence. The proposed system is tested against a set of sentences obtained from different sources like Wikipedia articles, TREC 2007dataset for question answering, and English book of grade II prep. The proposed model shows promising results in determining the question type with classification accuracy increases 95%, and also in generating (matching) the new question patterns with 87%.