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
- Venue:
- EventStory
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15–20
- Language:
- URL:
- https://aclanthology.org/W17-2703
- DOI:
- 10.18653/v1/W17-2703
- Cite (ACL):
- Evangelia Spiliopoulou, Eduard Hovy, and Teruko Mitamura. 2017. Event Detection Using Frame-Semantic Parser. In Proceedings of the Events and Stories in the News Workshop, pages 15–20, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Event Detection Using Frame-Semantic Parser (Spiliopoulou et al., EventStory 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W17-2703.pdf
- Data
- FrameNet