Chinese Named Entity Recognition Based on Rules and Conditional Random Field

Abstract

In the era of big data, data has gradually become an important productivity driving social progress. Accelerating the development and sharing of data resources is an inherent requirement of government transformation. Named entity recognition has a wide range of applications in the fields of information extraction and information retrieval, so its research is of great significance. Due to the complexity of Chinese structure and the lack of mature domestic corpora, the research on Chinese named entity recognition faces enormous challenges. Based on the analysis of the actual characteristics of named entities in Chinese text, this paper proposes a Chinese named entity recognition method based on rules and conditional random fields. The rule-based method is used to identify the named entities of digital expressions and time expressions. Named entity recognition of names of people, places, and organizations by combining rules and conditional random fields. Through the experiment, the results are analyzed, the best template is adjusted, and a complete and accurate Chinese named entity recognition method is designed and implemented. The experimental results show that the new method can effectively identify the named entities, improve the processing speed and efficiency, and has certain practical value.