Taxonomy-Guided Fine-Grained Entity Set Expansion

Jinfeng Xiao, Mohab Elkaref, Nathan Herr, Geeth De Mel, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Entity set expansion, the task of expanding a small set of similar entities into a much larger set, is a vital step for downstream tasks such as named entity recognition, knowledge base construction and information retrieval. Existing entity set expansion methods were developed by mainly considering entities at coarse-grained levels, which encounter difficulties for entity set expansion at fine-grained levels, due to the subtlety on fine-grained type inference and semantic drifting. In this study, we propose an automated (i.e. without human annotation), fine-grained set expansion framework, FGExpan, which utilizes a taxonomy structure and a pre-trained language model to achieve high performance. To facilitate our testing, a new fine-grained set expansion dataset is also constructed. Experiments on this dataset and those used in previous studies show that FGExpan achieves significantly better performance (MAP up by 0.176) on fine-grained types and also the state-of-the-art expansion quality on coarse-grained entity sets.

Original languageEnglish (US)
Title of host publication2023 SIAM International Conference on Data Mining, SDM 2023
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781611977653
StatePublished - 2023
Event2023 SIAM International Conference on Data Mining, SDM 2023 - Minneapolis, United States
Duration: Apr 27 2023Apr 29 2023

Publication series

Name2023 SIAM International Conference on Data Mining, SDM 2023


Conference2023 SIAM International Conference on Data Mining, SDM 2023
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Education
  • Information Systems


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