Evaluation on Network Social Media Named Entity Recognition Model Based on Active Learning

• Published in: ACM transactions on Asian and low-resource language information processing 2024-08, Vol.23 (8), p.1-13, Article 109
• Main Authors: He, Guijiao, Zhou, Yunfeng, Zheng, Yaodong
• Format: Article
• Language: English
• Subjects: Applied computing; Computing methodologies; Corporate surveillance; Discourse, dialogue and pragmatics; Information extraction; Language resources; Lexical semantics; Logical and relational learning; Machine learning; Model verification and validation; Modeling and simulation; Natural language processing; Patient privacy; Privacy policies; Social and professional topics; Sociology
• ISSN: 2375-4699; 2375-4702
• DOI: 10.1145/3600055

The medical security privacy and named entity recognition (NER) technology under the blockchain technology has been a hot topic in all walks of life. As a typical representative of medical security risks and NER, the NER model of online social media based on active learning has attracted worldwide attention. NER is an important part of natural language processing. Traditional recognition technology usually requires a lot of external information, and through the manual identification of its features, which costs a lot of time and energy. In order to solve the shortcomings of traditional recognition algorithms and the lack of feature extraction in network media NER, a new active learning model was introduced in this paper. In the information age, people are increasingly demanding a large amount of text information, and NER technology came into being. Its main function is to accurately identify important information from text and provide useful information for high-level work. The initial design of NER system is mainly based on the recognition of rules, so as to realize the recognition of named entities. However, in a complex network environment, it takes a lot of time and energy to establish rules without conflicts, and it has poor mobility. In recent years, with the continuous development of computer technology, the use of machine learning to actively learn the unknown information in the target area reduces the workload of manual annotation, thus realizing the active learning of large amounts of data. The research showed that the recognition accuracy under the traditional NER was low, and the information processing speed was slow; the accuracy rate of NER based on active learning was as high as 97%, and the speed of information processing had also been greatly improved, which had solved many problems under the traditional mode. User satisfaction could be as high as 95%, which showed that the latter had broad prospects. The progress of the new era cannot be separated from the support of new technologies. The research of this article has important guiding significance for medical security privacy and the application of NER under blockchain technology.