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{C}as{EE}: {A} Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Guo , Shu
;
Hei, Yiming
;
Li, Qian
;
Liu, Tingwen
;
Sheng, Jiawei
;
Wang , Lihong
;
Xu, Hongbo
;
Yu, Bowen
. - : Underline Science Inc., 2021
Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.14 Abstract: Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Most existing methods assume that events appear in sentences without overlaps, which are not applicable to the complicated overlapping event extraction. This work systematically studies the realistic event overlapping problem, where a word may serve as triggers with several types or arguments with different roles. To tackle the above problem, we propose a novel joint learning framework with cascade decoding for overlapping event extraction, termed as CasEE. Particularly, CasEE sequentially performs type detection, trigger extraction and argument extraction, where the overlapped targets are extracted separately conditioned on the specific former prediction. All the subtasks are jointly learned in a framework to capture dependencies among the subtasks. The evaluation on a public event extraction benchmark FewFC demonstrates that ...
Keyword:
Computational Linguistics
;
Condensed Matter Physics
;
Deep Learning
;
Electromagnetism
;
FOS Physical sciences
;
Information and Knowledge Engineering
;
Neural Network
;
Semantics
URL:
https://dx.doi.org/10.48448/8qhp-py25
https://underline.io/lecture/26105-casee-a-joint-learning-framework-with-cascade-decoding-for-overlapping-event-extraction
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