Deep Learning-Based Semantic Feature Extraction: A Literature Review and Future Directions

doi:10.12142/ZTECOM.202302003

Abstract

Abstract:

Semantic communication, as a critical component of artificial intelligence (AI), has gained increasing attention in recent years due to its significant impact on various fields. In this paper, we focus on the applications of semantic feature extraction, a key step in the semantic communication, in several areas of artificial intelligence, including natural language processing, medical imaging, remote sensing, autonomous driving, and other image-related applications. Specifically, we discuss how semantic feature extraction can enhance the accuracy and efficiency of natural language processing tasks, such as text classification, sentiment analysis, and topic modeling. In the medical imaging field, we explore how semantic feature extraction can be used for disease diagnosis, drug development, and treatment planning. In addition, we investigate the applications of semantic feature extraction in remote sensing and autonomous driving, where it can facilitate object detection, scene understanding, and other tasks. By providing an overview of the applications of semantic feature extraction in various fields, this paper aims to provide insights into the potential of this technology to advance the development of artificial intelligence.

Key words: semantic feature extraction, semantic communication, deep learning

DENG Letian, ZHAO Yanru. Deep Learning-Based Semantic Feature Extraction: A Literature Review and Future Directions[J]. ZTE Communications, 2023, 21(2): 11-17.

Figures/Tables 1

References 71

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