Research on Named Entity Recognition Method of Chinese Classics Under the Supervision of Domain Knowledge

Abstract

The current dominant named entity recognition methods of Chinese classics are classified as data-driven methods, which are limited by the data quality. The domain knowledge is introduced in this paper to supervise the process of the named entity recognition, so as to solve the poor performance problem because of the low-quality data. The experiments on the Historical Records corpus show that compared with the domain knowledge unsupervised case, the average accuracy, recall rate, and F1 value have respectively improved by 2.76%, 2.70%, and 2.75% under the supervision of domain knowledge. Domain knowledge plays an important role in improving the performance of the named entity recognition methods of Chinese classics.