Research Area:  Machine Learning

Abstract:

With the development of Web2.0, huge amount of text information is produced. It-s important to extract useful information from data. This paper systematically analyzes the main research progress and methods of named entity recognition (NER), and grasps the development context to help researchers quickly understand NER. [Method/process] We select representative literature for review, summarize and comb the mainstream methods by bibliometrics and literature research, and count the keywords of relevant papers in Web of Science to support this view, and finally summarize the applications and the development trends of NER. [Result/conclusion] Research shows that common recognition methods include rule-based, statistics-based, hybrid methods, and more and more tend to integrate multiple methods; in recent 5 years, hybrid and joint models based on deep learning are currently dominating the latest technology.

Keywords:  
Named Entity Recognition
Deep Learning
Machine Learning

Author(s) Name:  Xiaole Li, Tianyu Wang, Yadan Pang, Jin Han & Jin Shi

Publisher name:  Springer

DOI:  10.1007/978-3-031-06761-7_21

Volume Information:  

Paper Link:   https://link.springer.com/chapter/10.1007/978-3-031-06761-7_21