Priberam Machine Learning Lunch Seminar about Structured Sparsity for Structured Prediction
Speaker: André Martins (IST/UTL and CMU)
Venue: IST Alameda, Sala PA2 (Edifício de Pós-Graduação)
Date: Tuesday, February 7th, 2012
Time: 13:00
Lunch will be provided
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 hocfiltering 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 Mario Figueiredo, Pedro Aguiar, Noah Smith and Eric Xing.
Bio:
André Martins is a dual degree Ph.D. student in Language Technologies, at Instituto Superior Técnico and Carnegie Mellon University, in the scope of the Carnegie Mellon Portugal Program. His main research interests are machine learning, natural language processing, and optimization.
