PCFG models of linguistic tree representations
Computational Linguistics
Finite-state approximation of constraint-based grammars using left-corner grammar transforms
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A TAG-based noisy channel model of speech repairs
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
PCFGs with syntactic and prosodic indicators of speech repairs
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A syntactic time-series model for parsing fluent and disfluent speech
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Toward a psycholinguistically-motivated model of language processing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A framework for fast incremental interpretation during speech decoding
Computational Linguistics
A syntactic time-series model for parsing fluent and disfluent speech
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Positive results for parsing with a bounded stack using a model-based right-corner transform
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Improved syntactic models for parsing speech with repairs
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Word buffering models for improved speech repair parsing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
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This paper describes an incremental approach to parsing transcribed spontaneous speech containing disfluencies with a Hierarchical Hidden Markov Model (HHMM). This model makes use of the right-corner transform, which has been shown to increase non-incremental parsing accuracy on transcribed spontaneous speech (Miller and Schuler, 2008), using trees transformed in this manner to train the HHMM parser. Not only do the representations used in this model align with structure in speech repairs, but as an HMM-like time-series model, it can be directly integrated into conventional speech recognition systems run on continuous streams of audio. A system implementing this model is evaluated on the standard task of parsing the Switchboard corpus, and achieves an improvement over the standard baseline probabilistic CYK parser.