Automatic Question Answering System Based on Convolutional Neural Network and Its Application to Waste Collection System

Abstract

As a typical cyber-physical-social system (CPSS), the waste collection system profoundly changes the current waste processing mode and greatly relieves the dilemma of waste disposal. However, the existing waste collection system does not provide the function that guides people to deliver the waste into the correct trash bin. In order to improve the efficiency of waste collection system, we propose an automatic question answering system based on convolutional neural network (CNN) to help people classify waste correctly. The construction process of automatic question answering system is divided into the following steps. We first construct a question answering dataset about waste classification, in which question answering pairs from the four waste categories (recyclable waste, harmful waste, dry waste, and wet waste) are included. After the dataset is constructed, we perform text preprocessing on the dataset, which includes denoising, Chinese word segmentation, and removing stop words. After text preprocessing, we use the Word2vec model as feature representation. Then, we construct a CNN and utilize the word embeddings as an input to train model. Finally, we deploy the trained model to the waste collection system, which can answer the question of waste classification that people ask. We also present a comparative analysis of the proposed method and traditional machine learning methods. The experiment shows that the proposed method has higher accuracy of waste classification than that of traditional machine learning methods.