Entity recognition based on lexicon information and GlobalPointer
In order to improve the entity boundary differentiation performance of GlobalPointer,an entity recognition method integrating lexicon information and globalpointer was proposed to enhance the recognition performance.For characters,softlexcion was used to extract vocabulary features and combine them with characters.BiLSTM network and RoPE code were used to capture timing and relative position information to construct comprehensive features.Entity recognition was realized through entity matrix.Experiments were carried out on multiple datasets.Compared with other baseline models,the model had made some progress in the metrics of precision,recall and F1.The F1 in Weibo dataset had reached 71.33%,and the F1 in CMeEE dataset had reached 63.45%.It indicated that the model architecture could further expand semantic representation and enhance recognition performance.
relative position codelexicon informationentity identificationfeatures fusionneural network
万方数据
维普
