Research and Application of Semi-Supervised Entity Recognition Method in The Field of Technology Policy

In the field of technology policy, a large number of technology policies are released every day, and scientific researchers need to always pay attention to a great number of technology policy information on different websites, and it is arduous to find crucial policy information from them. Using named entity recognition technology to convert a great number of unstructured text information in technology policy fields into structured information can help scientific researchers obtain crucial policy information. Compared with named entity recognition in the general field, the main challenge of entity recognition in the professional field is that there is less data in the professional field with annotations. In order to reduce the resource overhead of annotated data, a semi-supervised learning method for named entity recognition is produced. The advantage of the semi-supervised learning training model is that it can use the text data with label information and the text data without label information to train the recognition model, and improve the generalization ability of the named entity recognition model. This paper innovatively proposes a dynamic adversarial training method DAT (Dynamic Adversarial Training) that dynamically adjusts the loss weights of supervised data and unsupervised data, and applies it to semi-supervised entity recognition tasks, and proposes the DAT-Bert-CRF model. Effectively solve the problem of semi-supervised entity recognition. The result of our experiment show that compared with other semi-supervised entity recognition methods, the performance of our model in this paper is better.