Constraint-driven rank-based learning for information extraction

Authors:
Sameer Singh;Limin Yao;Sebastian Riedel;Andrew McCallum
Affiliations:
University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA
Venue:
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2010

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Abstract

Most learning algorithms for undirected graphical models require complete inference over at least one instance before parameter updates can be made. SampleRank is a rank-based 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.