[Research purpose]Patent entity extraction is the basis of technology recognition from patent texts.At present,patent entity extraction is faced with the problem of low automation and accuracy.This study intended to improve this problem from two aspects:one is to establish a high-quality patent corpus in a specific field,and the other is to apply an advanced algorithm model to patent entity extrac-tion.[Research method]In this regard,a fine-grained information system was defined which contained13 entity types and the titles and abstracts of 921 patents in the field of brain-inspired intelligence were manually marked according to the annotation rules.Then a Bert-BiLSTM-CRF model which integrates deep learning and machine learning was used to identify the brain-inspired intelligence patent enti-ties.[Research conclusion]The model achieved accuracy rate,recall rate and F1 value of 0.8 on the whole and entities performed differ-ently according to their types.In order to verify the performance of the model,several comparative experiments were designed.The results showed that fine-tuning data and increasing training scale could improve the performance of the model.Moreover,the model is superior to some classical models during the same period.
关键词
专利实体/专利文本/专利挖掘/技术识别/深度学习/机器学习/Bert-BiLSTM-CRF模型
Key words
patent entity/patent text/patent mining/technology recognition/deep learning/machine learning/Bert-BiLSTM-CRF model
万方数据