Creating and sharing knowledge for telecommunications

Structured sparsity in structured prediction

Martins, A. ; Smith, N. A. ; Aguiar, P. ; Figueiredo, M. A. T.

Structured sparsity in structured prediction, Proc Empirical Methods in Language Processing - EMNLP, Edinburgh, United Kingdom, Vol. , pp. - , July, 2011.

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Abstract
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.