TY - GEN
T1 - Target-dependent Event Detection
T2 - 2021 IEEE International Conference on Big Data, Big Data 2021
AU - Zhang, Tiantian
AU - Mao, Xin
AU - Li, Dejian
AU - Ma, Meirong
AU - Yuan, Hao
AU - Zhu, Jianchao
AU - Lan, Man
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Event extraction aims to detect events and extract event arguments. However, various events are not only too nuanced and complex to distinguish, but also involve multiple entities in the real-world scenario, especially in the financial field. This brings a great challenge to the current event extraction. To address these problems, previous event-centric methods detect events first and then extract arguments. Due to the diversity and complexity of events, event detection has a low performance, which is unfit for the huge amount of news in the real world. Given that the performance of named entity recognition (NER) is satisfactory, we shift our perspective from event-centric to target-centric view. In this paper, we propose a new task: target-dependent event detection (TDED), which aims to extract target entities and detect their corresponding events. We also propose a semantic and syntactic aware approach to support thousands of target entity extraction first and dozens of event types detection, that can be applied to massive corpora. Experimental results on a real-world Chinese financial dataset demonstrate that our model outperforms previous methods, especially in complex scenarios.
AB - Event extraction aims to detect events and extract event arguments. However, various events are not only too nuanced and complex to distinguish, but also involve multiple entities in the real-world scenario, especially in the financial field. This brings a great challenge to the current event extraction. To address these problems, previous event-centric methods detect events first and then extract arguments. Due to the diversity and complexity of events, event detection has a low performance, which is unfit for the huge amount of news in the real world. Given that the performance of named entity recognition (NER) is satisfactory, we shift our perspective from event-centric to target-centric view. In this paper, we propose a new task: target-dependent event detection (TDED), which aims to extract target entities and detect their corresponding events. We also propose a semantic and syntactic aware approach to support thousands of target entity extraction first and dozens of event types detection, that can be applied to massive corpora. Experimental results on a real-world Chinese financial dataset demonstrate that our model outperforms previous methods, especially in complex scenarios.
KW - entity recognition
KW - event detection
KW - event keywords
KW - syntactic dependency distance
KW - target-dependent
UR - https://www.scopus.com/pages/publications/85125347289
U2 - 10.1109/BigData52589.2021.9671529
DO - 10.1109/BigData52589.2021.9671529
M3 - 会议稿件
AN - SCOPUS:85125347289
T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
SP - 571
EP - 580
BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
A2 - Chen, Yixin
A2 - Ludwig, Heiko
A2 - Tu, Yicheng
A2 - Fayyad, Usama
A2 - Zhu, Xingquan
A2 - Hu, Xiaohua Tony
A2 - Byna, Suren
A2 - Liu, Xiong
A2 - Zhang, Jianping
A2 - Pan, Shirui
A2 - Papalexakis, Vagelis
A2 - Wang, Jianwu
A2 - Cuzzocrea, Alfredo
A2 - Ordonez, Carlos
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2021 through 18 December 2021
ER -