Abstract
Event extraction is a difficult information extraction task. Li et al. (2014) explore the benefits of modeling event extraction and two related tasks, entity mention and relation extraction, jointly. This joint system achieves state-of-the-art performance in all tasks. However, as a system operating only at the sentence level, it misses valuable information from other parts of the document. In this paper, we present an incremental easy-first approach to make the global context of the entire document available to the intra-sentential, state-of-the-art event extractor. We show that our method robustly increases performance on two datasets, namely ACE 2005 and TAC 2015.- Anthology ID:
- C16-1215
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2279–2289
- Language:
- URL:
- https://aclanthology.org/C16-1215
- DOI:
- Cite (ACL):
- Alex Judea and Michael Strube. 2016. Incremental Global Event Extraction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2279–2289, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Incremental Global Event Extraction (Judea & Strube, COLING 2016)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/C16-1215.pdf