Topic-Oriented Open Relation Extraction with A Priori Seed Generation
Linyi Ding, Jinfeng Xiao, Sizhe Zhou, Chaoqi Yang, Jiawei Han · 2024
The field of open relation extraction (ORE) has recently observed significant advancement thanks to the growing capability of large language models (LLMs).Nevertheless, challenges persist when ORE is performed on specific topics.Existing methods give suboptimal results in five dimensions: factualness, topic relevance, informativeness, coverage, and uniformity.To improve topic-oriented ORE, we propose a zero-shot approach called Pri-ORE: Open Relation Extraction with a Priori seed generation.PriORE leverages the builtin knowledge of LLMs to maintain a dynamic seed relation dictionary for the topic.The dictionary is initialized by seed relations generated from topic-relevant entity types and expanded during contextualized ORE.PriORE then reduces the randomness in generative ORE by converting it to a more robust relation classification task.Experiments show the approach empowers better topic-oriented control over the generated relations and thus improves ORE performance along the five dimensions, especially on specialized and narrow topics. Dimension Extracted Relation Explanation Factualness gives toWrong given text and topic Relevance is exposed to Correct given text but not directly relevant to topic Informativeness form compound with Correct but conceptually too general given text and topic Coverage form powdery magnesium oxide Correct but too specific, covering too few instances Uniformity {be oxidized by, give electrons to, . . .} Multiple correct expressions extracted for the same relation