A Digitized Model of Document-Level Relationship Extraction

This paper constructs and evaluates a digital model of document-level relationship extraction, aiming to provide efficient and intelligent application support for the comprehensive evaluation of document-level relationships. In order to enable intelligent enterprises to integrate multiple data such as named entity recognition accuracy and information extraction efficiency, they can achieve unified management and real-time analysis of document-level relationship extraction data. In this paper, this paper first constructs a digital model of document-level relationship extraction based on intelligent algorithms, further strengthens and improves the digital model, and finally uses the intelligent algorithm system to identify and evaluate document-level relationships in real time. Experimental results show that the entity recognition efficiency of document-level relationships is significantly improved after using the model, especially the average increase of 11.54% and the error rate of triplet overlapping application are reduced by 21.08%. Comprehensive analysis shows that the model can effectively integrate the information intervention of document-level relationship, greatly improve the logical reasoning ability of intelligent enterprises, and provide accurate data support for information extraction management applications. It can be seen that the construction and evaluation of the digital model of document-level relationship extraction is reliable and practical.