A Short Text Classification Method Based on N‐Gram and CNN. Issue 2 (1st March 2020)

• Record Type: Journal Article
• Authors: Wang, Haitao; He, Jie; Zhang, Xiaohong; Liu, Shufen
• Is Part Of: Chinese journal of electronics. Volume 29:Issue 2(2020)
• Page Start: 248
• Page End: 254
• Publication Date: 2020-03-01
• Subjects: Short text -- Classification -- Convolution neural network -- N‐gram -- Concentration mechanism; learning (artificial intelligence) -- natural language processing -- neural nets -- pattern classification -- text analysis; short text classification method -- CNN -- Nature language process application -- existing research work -- explicate text representation -- implicit text representation -- shortness -- sparseness feature -- simple word vector feature -- traditional multisize filter Convolution neural network -- text classification task -- short text classification model -- abundant text feature -- N‐gram language model -- convolutional neural network -- classification result; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2020.01.001
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : Text classification is a fundamental task in Nature language process (NLP) application. Most existing research work relied on either explicate or implicit text representation to settle this kind of problems, while these techniques work well for sentence and can not simply apply to short text because of its shortness and sparseness feature. Given these facts that obtaining the simple word vector feature and ignoring the important feature by utilizing the traditional multi‐size filter Convolution neural network (CNN) during the course of text classification task, we offer a kind of short text classification model by CNN, which can obtain the abundant text feature by adopting none linear sliding method and N ‐gram language model, and picks out the key features by using the concentration mechanism, in addition employing the pooling operation can preserve the text features at the most certain as far as possible. The experiment shows that this method we offered, comparing the traditional machine learning algorithm and convolutional neural network, can markedly improve the classification result during the short text classification.