Topic-oriented open relation extraction with seed generation
Ding, Linyi
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Permalink
https://hdl.handle.net/2142/125722
Description
Title
Topic-oriented open relation extraction with seed generation
Author(s)
Ding, Linyi
Issue Date
2024-07-15
Director of Research (if dissertation) or Advisor (if thesis)
Han, Jiawei
Department of Study
Siebel Computing &DataScience
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
relation extraction
large language models
Abstract
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 sub-optimal results in five dimensions: factualness, topic relevance, informativeness, coverage, and uniformity. To improve topic-oriented ORE, we propose a zero-shot approach called PriORE: Open Relation Extraction with a Priori seed generation. The PriORE leverages the built-in knowledge of LLM to maintain a dynamic seed relation dictionary for the topic (which is initiated a priori). It initially generates seed relations from topic-relevant entity types and can be expanded during extraction. PriORE then converts the more random ORE task to a more robust relation classification task by comparing the relation dictionary to contexts. Experiments demonstrate this approach empowers better topic-oriented control over the generated relations and thus greatly improves ORE performance along the five dimensions, especially on specialized and narrow topics.
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