Paper: Constraint-Driven Rank-Based Learning for Information Extraction

ACL ID N10-1111
Title Constraint-Driven Rank-Based Learning for Information Extraction
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

Most learning algorithms for undirected graphical models require complete inference over at least one instance before parameter up- dates can be made. SampleRank is a rank- based learning framework that alleviates this problem by updating the parameters during in- ference. Most semi-supervised learning algo- rithms 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 compu- tational bottleneck. Different approaches to incorporate unlabeled data and prior knowl- edge 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...