Category Prediction of Questions Posted in Community-Based Question Answering Services Using Deep Learning Methods

Abstract

This paper presents methods of predicting categories of questions posted in community-based question answering (CQA) services using deep learning methods, which are implemented with stacked denoising autoencoders (SdA), as well as deep belief networks (DBN). We compare them with conventional machine learning methods, i.e., multi-layer perceptron (MLP) and support vector machines (SVM). We also compare their performance when using dropout regularization. The experimental results indicate that (1) the proposed methods reach much higher prediction precision than that provided by CQA services, (2) deep learning with dropout has higher prediction precision than the conventional machine learning methods, whether or not the dropout regularization is used, i.e., DBN with dropout reaches the highest precision and SdA with dropout reaches the next highest precision among all the methods in general, and the SdA with dropout in a specific case reaches the highest precision across all experiments, (3) increasing the dimensions of feature vectors representing the questions is an effective measure for improving the prediction precision, (4) prediction precision can be further improved using titles in addition to the actual questions and by improving the quality of the corpus used for training.