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- Title
CNN-VAE: An intelligent text representation algorithm.
- Authors
Xu, Saijuan; Guo, Canyang; Zhu, Yuhan; Liu, Genggeng; Xiong, Neal
- Abstract
Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively.
- Subjects
CONVOLUTIONAL neural networks; BIG data; MACHINE learning; POLYSEMY; SUPPORT vector machines; K-nearest neighbor classification; ALGORITHMS
- Publication
Journal of Supercomputing, 2023, Vol 79, Issue 11, p12266
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-023-05139-w