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COLING
2010

Semi-supervised Semantic Pattern Discovery with Guidance from Unsupervised Pattern Clusters

11 years 2 months ago
Semi-supervised Semantic Pattern Discovery with Guidance from Unsupervised Pattern Clusters
We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply this approach to the task of relation extraction. The evaluation results demonstrate that our novel bootstrapping procedure significantly outperforms a standard bootstrapping. Most importantly, our algorithm can effectively prevent semantic drift and provide semi-supervised learning with a natural stopping criterion.
Ang Sun, Ralph Grishman
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where COLING
Authors Ang Sun, Ralph Grishman
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