New models for improving supertag disambiguation
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
A salience driven approach to robust input interpretation in multimodal conversational systems
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A word clustering approach for language model-based sentence retrieval in question answering systems
Proceedings of the 18th ACM conference on Information and knowledge management
Estimation of stochastic context-free grammars and their use as language models
Computer Speech and Language
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A novel approach is presented to class-based language modeling based on part-of-speech statistics. It uses a deterministic word-to-class mapping, which handles words with alternative part-of-speech assignments through the use of ambiguity classes. The predictive power of word-based language models and the generalization capability of class-based language models are combined using both linear interpolation and word-to-class backoff, and both methods are evaluated. Since each word belongs to one precisely ambiguity class, an exact word-to-class backoff model can easily be constructed. Empirical evaluations on large-vocabulary speech-recognition tasks show perplexity improvements and significant reductions in word error-rate.