Jingdong Wang
ABSTRACT
As a crucial first step in the process of constructing knowledge graph, the accuracy of named entity recognition determines the construction effect of the final graph. However, at present, Chinese named entity recognition methods still have many problems, such as long training time and lower accuracy. Hence, we come up with a BERT-BILSTM-ACRF entity recognition method that combines the “self-attention” mechanism. To begin with, Bert model is selected as the embedding layer, the text is vectorized, and the character position in-formation is obtained through the bidirectional Long Short-Term Memory network. Secondly, the internal relationship of the character sequence is further searched through the self-attention mechanism, and finally the final optimal sequence is decoded by the conditional random field model. To check the effectiveness of the BERT-BILSTM-ACRF model, the model is applied to the data set of the university course textbook” Da-ta Structure”, and the result reaches 98.97%F1 value and 98.14%accuracy, which has good experimental results and certain practical value.
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Digital Library
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Index Terms
- Named Entities Based on the BERT-BILSTM-ACRF Model Recognition Research
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