An improved convolutional neural network for sentence classification based on term frequency and segmentation

Abstract

Recently, Sentence classification is a ubiquitous Natural Language Processing (NLP) task and deep learning is proved to be a kind of methods that has a significant effect in this area. In this work, we propose an improved Convolutional Neural Network (CNN) for sentence classification, in which a word-representation model is introduced to capture semantic features by encoding term frequency and segmenting sentence into proposals. The experimental results show that our methods outperform the state-of-the-art methods.