An Intelligent CNN-VAE Text Representation Technology Based on Text Semantics for Comprehensive Big Data

TL;DR

This paper introduces a novel CNN-VAE based text representation model that captures semantic features more effectively for NLP tasks, especially in big data contexts, outperforming traditional classifiers.

Contribution

It combines CNN and VAE with an improved word2vec to enhance semantic feature extraction and distinguish polysemy in text representation.

Findings

Outperforms KNN, RF, and SVM classifiers in experiments.

Effectively captures semantic features and polysemy.

Improves text classification accuracy.

Abstract

In the era of big data, a large number of text data generated by the Internet has given birth to a variety of text representation methods. In natural language processing (NLP), text representation transforms text into vectors that can be processed by computer without losing the original semantic information. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in language. Therefore, a text feature representation model based on convolutional neural network (CNN) and variational autoencoder (VAE) is proposed to extract the text features and apply the obtained text feature representation on the text classification tasks. CNN is used to extract the features of text vector to get the semantics among words and VAE is introduced to make the text feature space more consistent with Gaussian distribution. In addition, the output of the improved word2vec model is employed as the input of the proposed model to distinguish different meanings of the same word in different contexts. The experimental results show that the proposed model outperforms in k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) classification algorithms.