TY - GEN
T1 - Time Expression Normalization with Meta Time Information
AU - An, Mengyu
AU - Jin, Chenyu
AU - Zhong, Xiaoshi
AU - Cambria, Erik
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Time expression (a.k.a., timex) normalization is a fundamental task for many downstream researches and applications. Previous researches mainly developed deterministic rules and machine-learning methods for the end-to-end task of timex recognition and normalization (TERN). However, deterministic rules heavily depend on specific domains while machine-learning methods are somewhat unexplainable. To better understand the task, we analyze three diverse benchmark datasets for the characteristics of timex types and values. According to these characteristics, we propose a rule-based method termed MetaTime1 with three kinds of meta time information to normalize timexes into standard type and value formats. MetaTime is independent of specific domains and textual types. Experimental results on three diverse benchmark datasets demonstrate that MetaTime outperforms four representative state-of-the-art methods.
AB - Time expression (a.k.a., timex) normalization is a fundamental task for many downstream researches and applications. Previous researches mainly developed deterministic rules and machine-learning methods for the end-to-end task of timex recognition and normalization (TERN). However, deterministic rules heavily depend on specific domains while machine-learning methods are somewhat unexplainable. To better understand the task, we analyze three diverse benchmark datasets for the characteristics of timex types and values. According to these characteristics, we propose a rule-based method termed MetaTime1 with three kinds of meta time information to normalize timexes into standard type and value formats. MetaTime is independent of specific domains and textual types. Experimental results on three diverse benchmark datasets demonstrate that MetaTime outperforms four representative state-of-the-art methods.
KW - mapping relations
KW - meta time information
KW - priority relationship
KW - time expression normalization
KW - token triples
UR - http://www.scopus.com/inward/record.url?scp=85199969496&partnerID=8YFLogxK
U2 - 10.1109/CSCI62032.2023.00119
DO - 10.1109/CSCI62032.2023.00119
M3 - Conference contribution
AN - SCOPUS:85199969496
T3 - Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023
SP - 695
EP - 702
BT - Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023
Y2 - 13 December 2023 through 15 December 2023
ER -