Employing Lexicalized Dependency Paths for Active Learning of Relation Extraction

Huiyu Sun, Ralph Grishman

Research output: Contribution to journalArticlepeer-review


Active learning methods which present selected examples from the corpus for annotation provide more efficient learning of supervised relation extraction models, but they leave the developer in the unenviable role of a passive informant. To restore the developer’s proper role as a partner with the system, we must give the developer an ability to inspect the extraction model during development. We propose to make this possible through a representation based on lexicalized dependency paths (LDPs) coupled with an active learner for LDPs. We apply LDPs to both simulated and real active learning with ACE as evaluation and a year’s newswire for training and show that simulated active learning greatly reduces annotation cost while maintaining similar performance level of supervised learning, while real active learning yields comparable performance to the state-of-the-art using a small number of annotations.

Original languageEnglish (US)
Pages (from-to)1415-1423
Number of pages9
JournalIntelligent Automation and Soft Computing
Issue number3
StatePublished - 2022


  • lexicalized dependency paths
  • real active learning
  • Relation extraction
  • rule-based learning
  • simulated active learning

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence


Dive into the research topics of 'Employing Lexicalized Dependency Paths for Active Learning of Relation Extraction'. Together they form a unique fingerprint.

Cite this