Analysis of Specific Annotation Methods for Identifying Fine-grained Addresses Based on BiLSTM-CRF model
This paper describes the recognition of human input addresses,using deep learning to recognize entity names of semantically similar addresses.Train the dataset with BiLSTM-CRF,determine the best possible address based on the previous and subsequent connections,extract features and classify massive addresses,and obtain a majority of F1 indicators above 91％.The model was applied to actual data to verify the effectiveness of the method.
named entity recognitionlong short-term memory networkconditional random fieldinformation extraction
NETL
NSTL
万方数据
