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NAACL
2010

Constraint-Driven Rank-Based Learning for Information Extraction

11 years 4 months ago
Constraint-Driven Rank-Based Learning for Information Extraction
Most learning algorithms for undirected graphical models require complete inference over at least one instance before parameter updates can be made. SampleRank is a rankbased learning framework that alleviates this problem by updating the parameters during inference. Most semi-supervised learning algorithms also perform full inference on at least one instance before each parameter update. We extend SampleRank to semi-supervised learning in order to circumvent this computational bottleneck. Different approaches to incorporate unlabeled data and prior knowledge into this framework are explored. When evaluated on a standard information extraction dataset, our method significantly outperforms the supervised method, and matches results of a competing state-of-the-art semi-supervised learning approach.
Sameer Singh, Limin Yao, Sebastian Riedel, Andrew
Added 14 Feb 2011
Updated 14 Feb 2011
Type Journal
Year 2010
Where NAACL
Authors Sameer Singh, Limin Yao, Sebastian Riedel, Andrew McCallum
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