Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic labeling of semantic roles
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Practical use of non-local features for statistical spoken language understanding
Computer Speech and Language
Multi-domain spoken language understanding with transfer learning
Speech Communication
Efficient inference of CRFs for large-scale natural language data
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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In this paper, we exploit non-local features as an estimate of long-distance dependencies to improve performance on the statistical spoken language understanding (SLU) problem. The statistical natural language parsers trained on text perform unreliably to encode non-local information on spoken language. An alternative method we propose is to use trigger pairs that are automatically extracted by a feature induction algorithm. We describe a light version of the inducer in which a simple modification is efficient and successful. We evaluate our method on an SLU task and show an error reduction of up to 27% over the base local model.