The texts of reactor design field contain a wealth of valuable information that needs to be mined,yet the unstructured form of storage poses great challenges for information extraction.Traditional information extraction methods based on artificial rules are difficult to produce efficiency in the processing of complex data,and artificial intelligence technology is needed to overcome these problems.This paper focuses on the text data of main reactor equipment,analyzes its data characteristics,and addresses the issue of single entity overlap encountered in information extraction.By incorporating the CasRel model with added relationship information and a relation-oriented module,the improved ROERE model is developed.Experimental validation across different models demonstrates that integrating relationship information and relation-oriented modules is an effective strategy,enabling more accurate and comprehensive identification and prediction of triples,thereby enhancing the accuracy and recall of information extraction for main reactor equipment.
维普
万方数据
