Event Extraction Using Distant Supervision

Kevin Reschke, Martin Jankowiak, Mihai Surdeanu, Christopher Manning, Daniel Jurafsky

Abstract
Distant supervision is a successful paradigm that gathers training data for information extraction systems by automatically aligning vast databases of facts with text. Previous work has demonstrated its usefulness for the extraction of binary relations such as a person’s employer or a film’s director. Here, we extend the distant supervision approach to template-based event extraction, focusing on the extraction of passenger counts, aircraft types, and other facts concerning airplane crash events. We present a new publicly available dataset and event extraction task in the plane crash domain based on Wikipedia infoboxes and newswire text. Using this dataset, we conduct a preliminary evaluation of four distantly supervised extraction models which assign named entity mentions in text to entries in the event template. Our results indicate that joint inference over sequences of candidate entity mentions is beneficial. Furthermore, we demonstrate that the Searn algorithm outperforms a linear-chain CRF and strong baselines with local inference.
Anthology ID:
L14-1091
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
4527–4531
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1127_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Kevin Reschke, Martin Jankowiak, Mihai Surdeanu, Christopher Manning, and Daniel Jurafsky. 2014. Event Extraction Using Distant Supervision. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 4527–4531, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Event Extraction Using Distant Supervision (Reschke et al., LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1127_Paper.pdf
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