A Cache-Based Natural Language Model for Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A statistical approach to machine translation
Computational Linguistics
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Class-based n-gram models of natural language
Computational Linguistics
Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
Automatic labeling of semantic roles
Computational Linguistics
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Journal of Machine Learning Research
A neural probabilistic language model
The Journal of Machine Learning Research
Tagging English text with a probabilistic model
Computational Linguistics
Probabilistic top-down parsing and language modeling
Computational Linguistics
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Generalizing automatically generated selectional patterns
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
A spelling correction program based on a noisy channel model
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Active learning for statistical natural language parsing
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Techniques to achieve an accurate real-time large-vocabulary speech recognition system
HLT '94 Proceedings of the workshop on Human Language Technology
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Contrastive estimation: training log-linear models on unlabeled data
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A hierarchical Bayesian language model based on Pitman-Yor processes
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Scalable training of L1-regularized log-linear models
Proceedings of the 24th international conference on Machine learning
A unified architecture for natural language processing: deep neural networks with multitask learning
Proceedings of the 25th international conference on Machine learning
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Dependency-based syntactic-semantic analysis with PropBank and NomBank
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Semi-supervised sequence modeling with syntactic topic models
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
PNNL: a supervised maximum entropy approach to word sense disambiguation
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Improved smoothing for N-gram language models based on ordinary counts
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Semi-supervised semantic role labeling using the latent words language model
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
A Maximum Likelihood Approach to Continuous Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
IEEE Transactions on Signal Processing
Complexity reduction in fixed-lag smoothing for hidden Markovmodels
IEEE Transactions on Signal Processing
Design of a linguistic statistical decoder for the recognition of continuous speech
IEEE Transactions on Information Theory
Representations for multi-document event clustering
Data Mining and Knowledge Discovery
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We present a new generative model of natural language, the latent words language model. This model uses a latent variable for every word in a text that represents synonyms or related words in the given context. We develop novel methods to train this model and to find the expected value of these latent variables for a given unseen text. The learned word similarities help to reduce the sparseness problems of traditional n-gram language models. We show that the model significantly outperforms interpolated Kneser-Ney smoothing and class-based language models on three different corpora. Furthermore the latent variables are useful features for information extraction. We show that both for semantic role labeling and word sense disambiguation, the performance of a supervised classifier increases when incorporating these variables as extra features. This improvement is especially large when using only a small annotated corpus for training.