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2011

Can Document Selection Help Semi-supervised Learning? A Case Study On Event Extraction

8 years 2 months ago
Can Document Selection Help Semi-supervised Learning? A Case Study On Event Extraction
Annotating training data for event extraction is tedious and labor-intensive. Most current event extraction tasks rely on hundreds of annotated documents, but this is often not enough. In this paper, we present a novel self-training strategy, which uses Information Retrieval (IR) to collect a cluster of related documents as the resource for bootstrapping. Also, based on the particular characteristics of this corpus, global inference is applied to provide more confident and informative data selection. We compare this approach to self-training on a normal newswire corpus and show that IR can provide a better corpus for bootstrapping and that global inference can further improve instance selection. We obtain gains of
Shasha Liao, Ralph Grishman
Added 24 Aug 2011
Updated 24 Aug 2011
Type Journal
Year 2011
Where ACL
Authors Shasha Liao, Ralph Grishman
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