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arXiv 2008.12522cs.LGstat.ML
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面向大数据的基于文本语义的智能CNN-VAE文本表示技术

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

College of Mathematics and Computer Science, Fuzhou University(福州大学数学与计算机科学学院);Department of Business and Computer Science, Southwestern Oklahoma State University(西南俄克拉荷马州立大学商业与计算机科学系)

Genggeng Liu, Canyang Guo, Lin Xie, Wenxi Liu, Naixue Xiong, Guolong Chen

摘要:

在大数据时代,互联网产生的大量文本数据催生了多种文本表示方法。在自然语言处理中,文本表示是将文本转换成可被计算机处理的向量,而不会丢失原始的语义信息。然而,这些方法都难以有效地提取词间的语义特征,区分语言中的多义现象。为此,提出了一种基于卷积神经网络(CNN)和变分自编码器(VAE)的文本特征表示模型,用于提取文本特征,并将获得的文本特征表示应用于文本分类任务。利用CNN提取文本向量的特征,得到词与词之间的语义关系,并引入VAE使文本特征空间更符合高斯分布。此外,改进的word 2 vec模型的输出被用作所提出的模型的输入,以区分同一个词在不同的上下文中的不同含义。实验结果表明,该模型在k-近邻(KNN)、随机森林(RF)和支持向量机(SVM)分类算法中具有较好的分类性能。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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