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Constraint-driven rank-based learning for information extraction

Singh, S; Yao, L; Riedel, S; McCallum, A; (2010) Constraint-driven rank-based learning for information extraction. In: NAACL HLT 2010 - Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference. (pp. 729 - 732).

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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 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. © 2010 Association for Computational Linguistics.

Type:Proceedings paper
Title:Constraint-driven rank-based learning for information extraction
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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