Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters
COLING (Posters)(2010)
摘要
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.
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关键词
use cluster membership,distributional similarity,evaluation result,standard bootstrapping,semi-supervised semantic pattern discovery,semi-supervised pattern discovery,semantic drift,relation extraction,unsupervised pattern cluster,simple algorithm,semantic pattern,semi supervised learning
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