A corpus-based approach to language learning
A corpus-based approach to language learning
Statistical Language Learning
Improving Parsing of Spontaneous Speech with the Help of Prosodic Boundaries
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Glr*: a robust grammar-focused parser for spontaneously spoken language
Glr*: a robust grammar-focused parser for spontaneously spoken language
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
A bag of useful techniques for efficient and robust parsing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Fast LR parsing using rich (Tree Adjoining) Grammars
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
An alternative method of training probabilistic LR parsers
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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This paper describes a hybrid approach to spontaneous speech parsing. The implemented parser uses an extended probabilistic LR parsing model with rich context and and its output is post-processed by a symbolic tree transformation routine that tries to eliminate systematic errors of the parser. The parser has been trained for three different languages and was successfully integrated in the Verbmobil speech-to-speech translation system. The parser achieves more than 90%/90% labeled precision/recall on parsed Verbmobil utterances while 3% of German and 5% of all English input cannot be parsed.