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 L 1 -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.
In a more recent application REF , structured sparsity was used to perform effective feature template selection on three natural language processing tasks (chunking, entity recognition, and dependency parsing).
Structured Sparsity in Structured Prediction
EMNLP
Computer Science