2 research outputs found
Semi-Supervised Event Extraction with Paraphrase Clusters
Supervised event extraction systems are limited in their accuracy due to the
lack of available training data. We present a method for self-training event
extraction systems by bootstrapping additional training data. This is done by
taking advantage of the occurrence of multiple mentions of the same event
instances across newswire articles from multiple sources. If our system can
make a highconfidence extraction of some mentions in such a cluster, it can
then acquire diverse training examples by adding the other mentions as well.
Our experiments show significant performance improvements on multiple event
extractors over ACE 2005 and TAC-KBP 2015 datasets.Comment: NAACL 201