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Employing Lexicalized Dependency Paths for Active Learning of Relation Extraction

Huiyu Sun*, Ralph Grishman

New York University, New York, 10012, USA

* Corresponding Author: Huiyu Sun. Email:

Intelligent Automation & Soft Computing 2022, 34(3), 1415-1423.


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.


Cite This Article

H. Sun and R. Grishman, "Employing lexicalized dependency paths for active learning of relation extraction," Intelligent Automation & Soft Computing, vol. 34, no.3, pp. 1415–1423, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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