Chinese Named Entity Recognition Based on Label Information Fusion and Multi-task Learning
With the development of Chinese named entity recognition research,most models focus on enriching feature representa-tion by integrating vocabulary or glyph information but ignore label information.Therefore,a Chinese named entity recognition model integrating label information is proposed in this paper.Firstly,the embedding representation of characters is obtained by pre-trained model BERT-wwm,and labels are represented as vectors.The character representation and label representation are in-teractively learned by using the Transformer decoder structure to capture the interdependence between characters and labels and enrich the feature representation of characters.To promote the learning of label information,a supervision signal based on text sentences is constructed,multi-label text classification tasks are added,and multi-task learning is used for training.Among them,the named entity recognition task uses a conditional random field for decoding and prediction,and the multi-label text classifica-tion task uses a biaffine mechanism for decoding and prediction.The two tasks share all parameters except the decoding layer,which ensures that different supervision information is fed back to each subtask.Several groups of comparative experiments are carried out on the public data sets MSRA,Weibo,and Resume,and the F1 values of 95.75％,72.17％,and 96.23％are obtained respectively.Compared with several benchmark models,experimental result of the proposed model is improved to some extent,which validates its effectiveness and feasibility.
Named entity recognitionLabel informationAttention mechanismBiaffine mechanismPre-trained model
万方数据
维普
