A Named Entity Recognition Model Based on Multi-Task Learning and Cascading Pointer Network

Because, the existing named entity recognition models lack the Specificity of the field, and most of their combine the prediction of entity Location and entity category, which results in the accumulation of errors. So, named entity recognition model based on multi-task learning and pointer network is proposed, and innovations are made in the task Construction and domain entity information utilization in the model of Named entity recognition. This model is based on Transformer with multi-Head attention mechanism, and decomposes traditional tasks in entity recognition tasks and entity classification tasks, and carries out multi-tasks Task learning to reduce the accumulation of errors between tasks. Model in this paper also uses the similarity calculation based on the Comprehensive description of the entity category in entity classification Task for better pertinence of the domain entity. Experiments are conducted on public datasets and domain datasets to prove the advancement of the Model.