Neutrosophic Logic-Based Document Summarization

Nowadays, rich quantity of information is offered on the Net which makes it hard for the clients to detect necessary information. Programmed techniques are desirable to effectively filter and search useful data from the Net. The purpose of purported text summarization is to get satisfied content handling with information variety. The main factor of document summarization is to extract benefit feature. In this paper, we extract word feature in three group called important words. Also, we extract sentence feature depending on the extracted words. With increasing knowledge on the Internet, it turns out to be an extremely time-consuming, exhausting, and boring mission to read the whole content and papers and get the relevant information on precise topics

An Effective Method of Evaluating Pension Service Quality Using Multi-Dimension Attention Convolutional Neural Networks

How to make an accurate evaluation of the quality of pension service has become the most important task. However, in the real world, many customs always forget to rate pension service. They only leave a few short, less semantic, and discontinuous review words below the service. This paper will propose an effective multi-dimension attention convolutional neural networks (MACNNs) model to analyze customer review texts and predict the pension service quality. In MACNN, the emoticon feature, sentiment feature, and word feature can be extracted together to construct feature space. And then attention layer and convolution layer work together to predict the service quality. Compared with the traditional machine learning methods and neural network methods, this method is more objective and accurate to reflect consumers’ real evaluation of pension service.