Fine-Grained Event Trigger Detection

Duong Le, Thien Huu Nguyen


Abstract
Most of the previous work on Event Detection (ED) has only considered the datasets with a small number of event types (i.e., up to 38 types). In this work, we present the first study on fine-grained ED (FED) where the evaluation dataset involves much more fine-grained event types (i.e., 449 types). We propose a novel method to transform the Semcor dataset for Word Sense Disambiguation into a large and high-quality dataset for FED. Extensive evaluation of the current ED methods is conducted to demonstrate the challenges of the generated datasets for FED, calling for more research effort in this area.
Anthology ID:
2021.eacl-main.237
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2745–2752
Language:
URL:
https://aclanthology.org/2021.eacl-main.237
DOI:
10.18653/v1/2021.eacl-main.237
Bibkey:
Cite (ACL):
Duong Le and Thien Huu Nguyen. 2021. Fine-Grained Event Trigger Detection. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2745–2752, Online. Association for Computational Linguistics.
Cite (Informal):
Fine-Grained Event Trigger Detection (Le & Nguyen, EACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2021.eacl-main.237.pdf