Paper: Quadratic Features and Deep Architectures for Chunking

ACL ID N09-2062
Title Quadratic Features and Deep Architectures for Chunking
Venue Human Language Technologies
Session Short Paper
Year 2009
Authors

We experiment with several chunking models. Deeper architectures achieve better gener- alization. Quadratic filters, a simplification of a theoretical model of V1 complex cells, reliably increase accuracy. In fact, logistic regression with quadratic filters outperforms a standard single hidden layer neural network. Adding quadratic filters to logistic regression is almost as effective as feature engineering. Despite predicting each output label indepen- dently, our model is competitive with ones that use previous decisions.