Continual relation extraction aims to learn newly arriving relation types while preserving previously acquired knowledge,but it remains challenging due to catastrophic forgetting and severe confusion among semanticall...Continual relation extraction aims to learn newly arriving relation types while preserving previously acquired knowledge,but it remains challenging due to catastrophic forgetting and severe confusion among semantically similar relations.To address this problem,we propose a continual relation extraction method that integrates prompt learning,feature enhancement,anchoring loss,and similarity-aware prototype contrastive learning.Specifically,relation-specific soft prompts and dynamic prompt selection are introduced to provide targeted semantic guidance for different samples.Based on the matched prompts,both relation prototypes and sample representations are further enhanced to improve feature quality and discriminability.In addition,an anchoring loss and a similarity-aware prototype contrastive objective are designed to explicitly optimize the boundaries between semantically similar relations during memory replay.Experiments on the FewRel and TACRED datasets show that the proposed method consistently outperforms representative baseline methods,especially in later task stages where continual learning becomes more challenging.Ablation studies and visualization results further verify the effectiveness of prompt-guided feature enhancement and similar-relationaware optimization.These results indicate that explicitly strengthening fine-grained semantic boundaries,in addition to preserving historical knowledge,is important for continual relation extraction,and the proposed method provides an effective solution for improving both knowledge retention and relation discrimination.展开更多
This paper provides a new connection between algebraic hyperstructures and fuzzy sets. More specifically, using both properties of fuzzy topological spaces and those of fuzzy subhypergroups, we define the notions of l...This paper provides a new connection between algebraic hyperstructures and fuzzy sets. More specifically, using both properties of fuzzy topological spaces and those of fuzzy subhypergroups, we define the notions of lower （upper） fuzzy topological subhypergroups of a hypergroup endowed with a fuzzy topology. Some results concerning the image and the inverse image of a lower （upper） topological subhypergroup under a very good homomorphism of hypergroups （endowed with fuzzy topologies） are pointed out.展开更多
文摘Continual relation extraction aims to learn newly arriving relation types while preserving previously acquired knowledge,but it remains challenging due to catastrophic forgetting and severe confusion among semantically similar relations.To address this problem,we propose a continual relation extraction method that integrates prompt learning,feature enhancement,anchoring loss,and similarity-aware prototype contrastive learning.Specifically,relation-specific soft prompts and dynamic prompt selection are introduced to provide targeted semantic guidance for different samples.Based on the matched prompts,both relation prototypes and sample representations are further enhanced to improve feature quality and discriminability.In addition,an anchoring loss and a similarity-aware prototype contrastive objective are designed to explicitly optimize the boundaries between semantically similar relations during memory replay.Experiments on the FewRel and TACRED datasets show that the proposed method consistently outperforms representative baseline methods,especially in later task stages where continual learning becomes more challenging.Ablation studies and visualization results further verify the effectiveness of prompt-guided feature enhancement and similar-relationaware optimization.These results indicate that explicitly strengthening fine-grained semantic boundaries,in addition to preserving historical knowledge,is important for continual relation extraction,and the proposed method provides an effective solution for improving both knowledge retention and relation discrimination.
基金partially supported by Natural Innovation Term of Higher Education of Hubei Provinceof China(Grant No.T201109)
文摘This paper provides a new connection between algebraic hyperstructures and fuzzy sets. More specifically, using both properties of fuzzy topological spaces and those of fuzzy subhypergroups, we define the notions of lower （upper） fuzzy topological subhypergroups of a hypergroup endowed with a fuzzy topology. Some results concerning the image and the inverse image of a lower （upper） topological subhypergroup under a very good homomorphism of hypergroups （endowed with fuzzy topologies） are pointed out.
