A Text Semantic Matching Model Based on Knowledge Enhancement
Text semantic matching models have been widely used in information retrieval,text mining,and other fields.Current methods mainly predict the semantic relationship of text pairs from the perspective of the text itself,ignoring external knowledge.We propose a new text semantic matching model based on knowledge enhancement to address this issue.The model utilizes knowledge graph entities as external knowledge,effectively models the text's external knowledge information,and adaptively filters the noise in the external knowledge.Our model achieves the best results for most indicators in Natural Language Inference and Paraphrase Identification.This research will aid in the application of knowledge graphs in text semantic matching tasks and provide a reference for applying knowledge graphs to the information field.
text semantic matchinginformation retrievalknowledge graphknowledge enhancement
NETL
NSTL
万方数据
维普
