TY - GEN
T1 - Interactive Attention Network for Chinese Address Element Recognition
AU - Bi, Yusheng
AU - Tian, Lihua
AU - Li, Chen
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Existing Named Entity Recognition (NER) models have achieved good performance, but they have low accuracy in Chinese address element recognition tasks. After analysis, we believe that the boundary information of the address text is more sensitive than the general text, and the sentences are independent of each other, unlike the general paragraph-style text with contextual connections. On the other hand, the previous NER models rarely consider the use of interaction between subtasks to enhance the performance of the NER task.This paper proposes an Interactive Attention Network (IAN) model, which uses boundary-based information and type-based information to improve NER task performance and introduces an interaction mechanism to share information between each subtask. In addition, a boundary auxiliary module is added to obtain explicit boundary information. The experimental results show that the proposed IAN model can solve the address element recognition task more effectively.
AB - Existing Named Entity Recognition (NER) models have achieved good performance, but they have low accuracy in Chinese address element recognition tasks. After analysis, we believe that the boundary information of the address text is more sensitive than the general text, and the sentences are independent of each other, unlike the general paragraph-style text with contextual connections. On the other hand, the previous NER models rarely consider the use of interaction between subtasks to enhance the performance of the NER task.This paper proposes an Interactive Attention Network (IAN) model, which uses boundary-based information and type-based information to improve NER task performance and introduces an interaction mechanism to share information between each subtask. In addition, a boundary auxiliary module is added to obtain explicit boundary information. The experimental results show that the proposed IAN model can solve the address element recognition task more effectively.
KW - Address Element Recognition
KW - Boundary Auxiliary Module
KW - Chinese NER
KW - Interaction Mechanism
KW - Named Entity Recognition
UR - https://www.scopus.com/pages/publications/85125196190
U2 - 10.1109/UEMCON53757.2021.9666557
DO - 10.1109/UEMCON53757.2021.9666557
M3 - 会议稿件
AN - SCOPUS:85125196190
T3 - 2021 IEEE 12th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2021
SP - 390
EP - 395
BT - 2021 IEEE 12th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2021
A2 - Paul, Rajashree
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2021
Y2 - 1 December 2021 through 4 December 2021
ER -