Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Parsing spoken language: a semantic caseframe approach
COLING '86 Proceedings of the 11th coference on Computational linguistics
Robust parsing of severely corrupted spoken utterances
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 1
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 2
Perspectives of DBMT for monolingual authors on the basis of LIDIA-1, an implemented mock-up
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
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This paper introduces a robust interactive method for speech understanding. The generalized LR parsing is enhanced in this approach. Parsing proceeds from left to right correcting minor errors. When a very noisy portion is detected, the parser skips that portion using a fake nonterminal symbol. The unidentified portion is resolved by re-utterance of that portion which is parsed very efficiently by using the parse record of the first utterance. The user does not have to speak the whole sentence again. This method is also capable of handling unknown words, which is important in practical systems. Detected unknown words can be incrementally incorporated into the dictionary after the interaction with the user. A pilot system has shown great effectiveness of this approach.