Open Access iconOpen Access

ARTICLE

crossmark

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: email

Intelligent Automation & Soft Computing 2022, 34(3), 1415-1423. https://doi.org/10.32604/iasc.2022.030794

Abstract

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.

Keywords


Cite This Article

APA Style
Sun, H., Grishman, R. (2022). Employing lexicalized dependency paths for active learning of relation extraction. Intelligent Automation & Soft Computing, 34(3), 1415-1423. https://doi.org/10.32604/iasc.2022.030794
Vancouver Style
Sun H, Grishman R. Employing lexicalized dependency paths for active learning of relation extraction. Intell Automat Soft Comput . 2022;34(3):1415-1423 https://doi.org/10.32604/iasc.2022.030794
IEEE Style
H. Sun and R. Grishman, "Employing Lexicalized Dependency Paths for Active Learning of Relation Extraction," Intell. Automat. Soft Comput. , vol. 34, no. 3, pp. 1415-1423. 2022. https://doi.org/10.32604/iasc.2022.030794



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
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.
  • 2103

    View

  • 1509

    Download

  • 0

    Like

Share Link