Structured Sparsity for Structured Prediction

7 February 2012

André Martins IST/UTL and CMU

Linear models have enjoyed great success in structured prediction in NLP. While a lot of progress has been made on efficient training with several loss functions, the problem of endowing learners with a mechanism for feature selection is still unsolved. Common approaches employ ad hoc filtering or L1-regularization; both ignore the structure of the feature space, preventing practicioners from encoding structural prior knowledge. We fill this gap by adopting regularizers that promote structured sparsity, along with efficient algorithms to handle them.

Experiments on three tasks (chunking, entity recognition, and dependency parsing) show gains in performance, compactness, and model interpretability.

This is joint work with Mário Figueiredo, Pedro Aguiar, Noah Smith and Eric Xing.



André Martins is a PhD student in Language Technologies, at Instituto Superior Técnico and Carnegie Mellon University. His main research interests are machine learning, natural language processing, and optimization.