Entity Recognition in Education Domain Based on Character Attention and Dictionary Feature
Aiming at the problem that the existing entity recognition methods do not consider the influence of education terms on the model recognition performance,which leads to poor model performance and fuzzy knowledge entity boundary,a new entity recognition method based on character attention and dictionary feature is proposed.In this method,word vectors are generated according to contextual semantic information through BERT preprocessing language model,and a character attention mechanism based on part of speech is proposed to redistribute the weight of words in sentences.Then,it is spliced and fused with the features of the educational field dictionary constructed,and input into BiLSTM network and IDCNN network to extract features.The output of the two layers is dynamically combined through the attention mechanism,and the output of the two layers is weighted to fuse new features.Finally,the label sequence corresponding to the entity is obtained through conditional random field calculation.Compared to existing methods,the proposed method achieves higher accuracy in an educational domain text corpus.The precision,recall,and F1 score of the recognition results are 90.71%,91.37%,and 91.04%,respectively.
entity recognitiondictionary featurecharacter attentionIDCNNconditional random field
国家科技期刊平台
NSTL
万方数据
