Research on Related Entity Recognition and Evolution Analysis of"Technology-Knowledge"Based on Deep Semantic Understanding
[Objective/Significance]Understand the"technology-knowledge"related entities from a fine-grained perspective,and construct a implementation scheme for identifying technology elements and knowledge elements in patent documents.[Methods/Processes]The traditional machine learning models HMM and CRF,and the deep learning models BiLSTM-CRF,BERT-Softmax,BERT-CRF,and BERT-BiLSTM-CRF are selected for the task training and learning in order to identify the fine-grained technology with optimal performance and the knowledge entity recognition model.[Results/Conclusions]In order to validate the theoretical framework of technology and knowledge entity recognition,this paper takes the text in the field of publishing and printing as the experimental validation scenario,randomly selects 7853 valid corpus sentences from patent texts,and annotates 71626 entities,and determines that the BERT-BiLSTM-CRF is the entity recognition model with better performance through training,and the F1 value of its comprehensive performance for knowledge and technology entity recognition is 0.82.In addition,this paper applies the trained optimal model to identify 4769296 pairs of knowledge-technology entity association combinations from the first claim,claims,independent claims and technical effects of 66665 patents,and analyzes the technology evolution paths and the evolution pattern of the"technology-knowledge"association network structure.We also analyzed the technology evolution path and the evolution law of"technology-knowledge"association network structure.
Scientific and Technological InformationSubject Technology CorrelationEntity RecognitionBERT
NETL
NSTL
万方数据
