Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching

Weiyi Lu, Thien Huu Nguyen


Abstract
Event detection (ED) and word sense disambiguation (WSD) are two similar tasks in that they both involve identifying the classes (i.e. event types or word senses) of some word in a given sentence. It is thus possible to extract the knowledge hidden in the data for WSD, and utilize it to improve the performance on ED. In this work, we propose a method to transfer the knowledge learned on WSD to ED by matching the neural representations learned for the two tasks. Our experiments on two widely used datasets for ED demonstrate the effectiveness of the proposed method.
Anthology ID:
D18-1517
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4822–4828
Language:
URL:
https://aclanthology.org/D18-1517
DOI:
10.18653/v1/D18-1517
Bibkey:
Cite (ACL):
Weiyi Lu and Thien Huu Nguyen. 2018. Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4822–4828, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching (Lu & Nguyen, EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1517.pdf
Video:
 https://aclanthology.org/D18-1517.mp4