Review of Transfer Learning for Named Entity Recognition

Named entity recognition (NER) is one of the core application tasks of natural language processing. Traditional and deep NER methods rely heavily on a large amount of labeled training data with the same distri-bution, and the portability of the model is very poor. However, data are small and personalized in practical appli-cations, and it is very difficult to collect enough training data. Transfer learning is used in NER, the data and model of the source domain are utilized to complete the target task model construction, increase the amount of labeled data in the target domain and reduce the demand for the amount of labeled data of the target model. It has a very good effect in dealing with the task of low-resource NER. Firstly, the method and difficulty of NER and the method of transfer learning are summarized. Then, transfer learning methods applied to NER including data-based transfer learning, model-based transfer learning and adversarial transfer learning in recent years are comprehensively reviewed, and adversarial transfer learning methods are mainly described. Finally, this paper further expounds the current problems and looks forward to the future research directions