Event Detection Using Frame-Semantic Parser

Evangelia Spiliopoulou, Eduard Hovy, Teruko Mitamura


Abstract
Recent methods for Event Detection focus on Deep Learning for automatic feature generation and feature ranking. However, most of those approaches fail to exploit rich semantic information, which results in relatively poor recall. This paper is a small & focused contribution, where we introduce an Event Detection and classification system, based on deep semantic information retrieved from a frame-semantic parser. Our experiments show that our system achieves higher recall than state-of-the-art systems. Further, we claim that enhancing our system with deep learning techniques like feature ranking can achieve even better results, as it can benefit from both approaches.
Anthology ID:
W17-2703
Volume:
Proceedings of the Events and Stories in the News Workshop
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
EventStory | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
15–20
Language:
URL:
https://www.aclweb.org/anthology/W17-2703
DOI:
10.18653/v1/W17-2703
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2703.pdf