Date of Original Version
Proceedings of the Annual Meeting of the Association for Computational Linguistics
Copyright 2014 Association for Computational Linguistics
Abstract or Description
We introduce three linguistically motivated structured regularizers based on parse trees, topics, and hierarchical word clusters for text categorization. These regularizers impose linguistic bias in feature weights, enabling us to incorporate prior knowledge into conventional bagof-words models. We show that our structured regularizers consistently improve classification accuracies compared to standard regularizers that penalize features in isolation (such as lasso, ridge, and elastic net regularizers) on a range of datasets for various text prediction problems: topic classification, sentiment analysis, and forecasting.
Proceedings of the Annual Meeting of the Association for Computational Linguistics, 786-796.