Paper: Structured Sparsity in Structured Prediction

ACL ID D11-1139
Title Structured Sparsity in Structured Prediction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011

Linear models have enjoyed great success in structured prediction in NLP. While a lot of progress has been made on efficient train- ing 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 algo- rithms to handle them. Experiments on three tasks (chunking, entity recognition, and de- pendency parsing) show gains in performance, compactness, and model interpretability.