Long Tail Relation

Long-tail relation extraction focuses on accurately identifying less frequent relationships between entities in text, a challenge stemming from the scarcity of training data for these uncommon relations. Current research emphasizes leveraging techniques like few-shot learning, incorporating external knowledge sources (e.g., sememes, knowledge graphs), and employing novel model architectures such as metric learning and nearest-neighbor search to improve performance on these under-represented relations. Successfully addressing this challenge is crucial for enhancing natural language understanding and knowledge graph completion, enabling more comprehensive and accurate information extraction from diverse text corpora.