Paper: Learning Soft Linear Constraints with Application to Citation Field Extraction

ACL ID P14-1056
Title Learning Soft Linear Constraints with Application to Citation Field Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Accurately segmenting a citation string into fields for authors, titles, etc. is a chal- lenging task because the output typically obeys various global constraints. Previous work has shown that modeling soft con- straints, where the model is encouraged, but not require to obey the constraints, can substantially improve segmentation per- formance. On the other hand, for impos- ing hard constraints, dual decomposition is a popular technique for efficient predic- tion given existing algorithms for uncon- strained inference. We extend dual decom- position to perform prediction subject to soft constraints. Moreover, with a tech- nique for performing inference given soft constraints, it is easy to automatically gen- erate large families of constraints and learn their costs with a simple convex o...