An Entity-Aware Adversarial Domain Adaptation Network for Cross-Domain Named Entity Recognition (Student Abstract)

Qi PENG, Changmeng ZHENG, Yi CAI*, Tao WANG, Haoran XIE, Qing LI

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference (Extended Abstracts)Abstractspeer-review

Abstract

Existing methods for named entity recognition are critically relied on labeled data. To handle the situation that the data is fully-unlabeled, we propose an entity-aware adversarial domain adaptation network, which utilizes the labeled source data and then adapts to unlabeled target domain. We first apply adversarial training to reduce the distribution gap between different domains. Furthermore, we introduce an entity-aware attention to guide adversarial process to achieve the alignment of entity features. The experiment shows that our model outperforms the state-of-the-art approaches.

Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAssociation for the Advancement of Artificial Intelligence
Pages15865-15866
Number of pages2
ISBN (Electronic)9781713835974
Publication statusPublished - Feb 2021
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Online, Virtual, Online
Duration: 2 Feb 20219 Feb 2021
https://ojs.aaai.org/index.php/AAAI/issue/view/399

Publication series

Name35th AAAI Conference on Artificial Intelligence, AAAI 2021
Volume18

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period2/02/219/02/21
Internet address

Bibliographical note

Funding Information:
This work was supported by the Fundamental Research Funds for the Central Universities, SCUT(No.D2182480), the National Key Research and Development Program of China, the Science and Technology Programs of Guangzhou (No.201802010027, 201902010046), National Natural Science Foundation of China (62076100).

Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved

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