TY - GEN
T1 - An Entity-Aware Adversarial Domain Adaptation Network for Cross-Domain Named Entity Recognition (Student Abstract)
AU - Peng, Qi
AU - Zheng, Changmeng
AU - Cai, Yi
AU - Wang, Tao
AU - Xie, Haoran
AU - Li, Qing
N1 - 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
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85130040069&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:85130040069
T3 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
SP - 15865
EP - 15866
BT - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
PB - Association for the Advancement of Artificial Intelligence
T2 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
Y2 - 2 February 2021 through 9 February 2021
ER -