Mathematical named entity recognition based on the combination of ChineseBERT and multi-feature network
To address the problems of insufficient feature extraction ability and inaccurate semantic expression of word vectors in the basic deep learning model,a mathematical named entity recognition model combining ChineseBERT and multi-feature network is proposed.ChineseBERT combines the context of the current word to dynamically adjust the vector representation and improve the accuracy of the semantic representation of the word vector.The multi-feature network captures the local and global sequence features of characters simultane-ously through the improved convolution network and the bidirectional simple recurrent unit.The soft attention mechanism recognizes the key features that have a great impact on entity recognition,and the recognition re-sults are output by the conditional random field.Experiments on real mathematical data sets show that the F1 score of the model reaches 97.67％,which is higher than the deep learning model with good performance in re-cent years.The training efficiency of simple recurrent unit is higher,which proves the effectiveness of the model.
named entity recognitionChineseBERTmulti-feature networkmulti-scale convolutionsoft attention
NETL
NSTL
万方数据
