Dictionary Enhancement and Radical Perception for Sheep Disease Named Entity Recognition
The rapid development of information technology has given rise to a vast and potentially valuable amount of informa-tion on sheep diseases.However,there are few researches on named entity recognition of sheep disease texts,and the general model is difficult to represent the semantic information of sheep disease.Compared with other fields,there are more unregistered words in named entity recognition of sheep disease.Based on this,a sheep disease entity recognition model with dictionary enhancement and radical perception is proposed.This method constructs a sheep disease dictionary,integrates the similarity weight matrix of BERT's underlying word vector and its matching to the vocabulary vector,deeply embeds sheep disease dictionary information in the under-lying layer,and improves the difficulty of characterizing sheep disease information in the universal BERT model.In addition,based on the convolutional neural network framework,the unique pictographic radical features of sheep disease entities are extracted.Re-cursive disassembly of character radicals is used to concatenate and map the final extracted radical features with BERT output fea-ture sequences to the lower BiLSTM-CRF model input layer,improving the boundary awareness of sheep disease entities.Through experiments,it has been proven that this model has higher adaptability in named entity recognition of sheep disease texts.
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