Abstract
Traditional event detection classifies a word or a phrase in a given sentence for a set of prede- fined event types. The limitation of such pre- defined set is that it prevents the adaptation of the event detection models to new event types. We study a novel formulation of event detec- tion that describes types via several keywords to match the contexts in documents. This fa- cilitates the operation of the models to new types. We introduce a novel feature-based attention mechanism for convolutional neural networks for event detection in the new for- mulation. Our extensive experiments demon- strate the benefits of the new formulation for new type extension for event detection as well as the proposed attention mechanism for this problem- Anthology ID:
- D19-5532
- Volume:
- Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 243–248
- Language:
- URL:
- https://aclanthology.org/D19-5532
- DOI:
- 10.18653/v1/D19-5532
- Cite (ACL):
- Viet Dac Lai and Thien Nguyen. 2019. Extending Event Detection to New Types with Learning from Keywords. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 243–248, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Extending Event Detection to New Types with Learning from Keywords (Lai & Nguyen, WNUT 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D19-5532.pdf