Paper: A Discriminative Model for Query Spelling Correction with Latent Structural SVM

ACL ID D12-1138
Title A Discriminative Model for Query Spelling Correction with Latent Structural SVM
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

Discriminative training in query spelling cor- rection is difficult due to the complex inter- nal structures of the data. Recent work on query spelling correction suggests a two stage approach a noisy channel model that is used to retrieve a number of candidate corrections, followed by discriminatively trained ranker applied to these candidates. The ranker, how- ever, suffers from the fact the low recall of the first, suboptimal, search stage. This paper proposes to directly optimize the search stage with a discriminative model based on latent structural SVM. In this model, we treat query spelling correction as a multi- class classification problem with structured in- put and output. The latent structural informa- tion is used to model the alignment of words in the spelling correction proc...