Triplet Extraction

Triplet extraction focuses on identifying sets of three related elements (e.g., entity, relation, entity) within unstructured text, aiming to structure information for knowledge graph construction or enhanced question answering. Recent research emphasizes improving accuracy and efficiency through techniques like contrastive learning to better capture relationships between elements, and leveraging large language models for zero- or few-shot learning, often incorporating span-based tagging and greedy inference strategies. These advancements are significant for improving knowledge base construction, sentiment analysis (particularly aspect-based sentiment triplet extraction), and question answering systems that rely on structured knowledge representation.