Deep Learning and Its Applications in Anomaly Detection and Natural Language Processing

Dear Colleagues,

Natural Language Processing (NLP) and anomaly detection are key branches of deep learning. NLP focuses on enabling machines to understand the human language. Anomaly detection aims to identify the unexpected items or events in data sets, and has been widely applied in fraud detection, network intrusion detection, and cancer detection. Recently, a lot of effort in NLP and anomaly detection has achieved remarkable success in tasks, such as question answering, machine translation, smart assistants, and fraud detection. Pre-trained language models, such as BERT, GPT-3, and ChatGPT, have been widely applied in NLP and anomaly detection. They are also crucial to a wide range of other research topics, for biomedical information processing, knowledge graph, and multimodal intelligence. However, numerous relevant unsolved theoretical and technological problems await further research. We welcome original research articles reporting the development of novel ideas, models, and algorithms on deep learning, and their application in anomaly detection and natural language processing.

This Special Issue welcomes submissions covering a wide range of topic areas (though not limited to these):

• Deep learning/Machine learning;
• Anomaly detection;
• Named entity recognition;
• Relation extraction;
• Question answering;
• Machine translation;
• knowledge graph;
• Disambiguation;
• Summarization.

Prof. Dr. Jiang Zhong
Prof. Dr. Ying Xie
Dr. Weitong Chen
Prof. Dr. Xue Li
Guest Editors

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