Efficient Hybrid Technique for Community Question Answering

Abstract

Question answering communities (QAC) are nowadays becoming widely used due to the huge facilities and flow of information that it provides. These communities target is to share and exchange the knowledge between users. Through asking and answering questions under large number of categories. Unfortunately, there are a lot of issues existing that made knowledge process became a difficult one. One of those issues is that not every asker has the knowledge and ability to select the best answer for his question, or even selecting the best answer based on subjective matters. The analysis in this thesis is conducted on stack overflow community. In this work, a hybrid model for predicting the best answer is proposed. The proposed model is consisting of two modules. The first module is the content feature which consists of three types of features: question-answer features, answer content features, and answer-answer features. In the second module, a novel reputation score function to stack overflow community is used as a non-content feature to predict the best answer. Then, a hybrid model is proposed that merge content and non-content models and use them in the prediction. Study conducted experiments to train three different classifiers using the new added features. The prediction accuracy in the content and the proposed hybrid model i