An Efficient Model for Finding and Ranking Related Questions in Community Question Answering Systems

Abstract

The task of finding- and ranking-related questions plays the most important role for any real-world Community Question Answering (cQA) systems. This paper proposes a new method to solve this problem by considering multi-views for measuring the similarities between the input questions and the question-answering pairs in the database. Our model will investigate various aspects for understanding questions. Beside the traditional features such as bag of n-grams, we will use more efficient aspects that include word embeddings and question categories. We will use a word representation model for generating word embeddings, a question classification module for determining the category for an input question. Then all these obtained features are combined into a machine learning-based framework for getting similarity existing question-answering pairs as well as for ranking these pairs. We tested our proposed approach on the dataset SemEval 2016 and the experiment shows obtained results with the Accuracy and MAP of 80.43% and 77.43%, respectively, which are the highest accuracies in comparison with previous studies.