TY - GEN
T1 - ZH-NER
T2 - 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021
AU - Zhu, Peng
AU - Cheng, Dawei
AU - Yang, Fangzhou
AU - Luo, Yifeng
AU - Qian, Weining
AU - Zhou, Aoying
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - NER is challenging because of the semantic ambiguities in academic literature, especially for non-Latin languages. Besides, recognizing Chinese named entities needs to consider word boundary information, as words contained in Chinese texts are not separated with spaces. Leveraging word boundary information could help to determine entity boundaries and thus improve entity recognition performance. In this paper, we propose to combine word boundary information and semantic information for named entity recognition based on multi-task adversarial learning. We learn common shared boundary information of entities from multiple kinds of tasks, including Chinese word segmentation (CWS), part-of-speech (POS) tagging and entity recognition, with adversarial learning. We learn task-specific semantic information of words from these tasks, and combine the learned boundary information with the semantic information to improve entity recognition, with multi-task learning. We conduct extensive experiments to demonstrate that our model achieves considerable performance improvements.
AB - NER is challenging because of the semantic ambiguities in academic literature, especially for non-Latin languages. Besides, recognizing Chinese named entities needs to consider word boundary information, as words contained in Chinese texts are not separated with spaces. Leveraging word boundary information could help to determine entity boundaries and thus improve entity recognition performance. In this paper, we propose to combine word boundary information and semantic information for named entity recognition based on multi-task adversarial learning. We learn common shared boundary information of entities from multiple kinds of tasks, including Chinese word segmentation (CWS), part-of-speech (POS) tagging and entity recognition, with adversarial learning. We learn task-specific semantic information of words from these tasks, and combine the learned boundary information with the semantic information to improve entity recognition, with multi-task learning. We conduct extensive experiments to demonstrate that our model achieves considerable performance improvements.
KW - Adversarial learning
KW - Chinese word segmentation
KW - Multi-task learning
KW - Named entity recognition
KW - Part-of-speech tagging
UR - https://www.scopus.com/pages/publications/85104793579
U2 - 10.1007/978-3-030-73197-7_40
DO - 10.1007/978-3-030-73197-7_40
M3 - 会议稿件
AN - SCOPUS:85104793579
SN - 9783030731960
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 603
EP - 611
BT - Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings
A2 - Jensen, Christian S.
A2 - Lim, Ee-Peng
A2 - Yang, De-Nian
A2 - Chang, Chia-Hui
A2 - Xu, Jianliang
A2 - Peng, Wen-Chih
A2 - Huang, Jen-Wei
A2 - Shen, Chih-Ya
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 11 April 2021 through 14 April 2021
ER -