A tree-based statistical language model for natural language speech recognition
Readings in speech recognition
Algorithms for bigram and trigram word clustering
Speech Communication
Machine Learning
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Factored language models and generalized parallel backoff
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Self-training PCFG grammars with latent annotations across languages
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
A joint language model with fine-grain syntactic tags
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Generalized interpolation in decision tree LM
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Advances in speech transcription at IBM under the DARPA EARS program
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 0.00 |
Decision trees have been applied to a variety of NLP tasks, including language modeling, for their ability to handle a variety of attributes and sparse context space. Moreover, forests (collections of decision trees) have been shown to substantially outperform individual decision trees. In this work, we investigate methods for combining trees in a forest, as well as methods for diversifying trees for the task of syntactic language modeling. We show that our tree interpolation technique outperforms the standard method used in the literature, and that, on this particular task, restricting tree contexts in a principled way produces smaller and better forests, with the best achieving an 8% relative reduction in Word Error Rate over an n-gram baseline.