Stochastic Context-Free Grammars for Island-Driven Probabilistic Parsing

Anna Corazza, Renato De Mori, Roberto Gretter, Giorgio Satta


Abstract
In automatic speech recognition the use of language models improves performance. Stochastic language models fit rather well the uncertainty created by the acoustic pattern matching. These models are used to score theories corresponding to partial interpretations of sentences. Algorithms have been developed to compute probabilities for theories that grow in a strictly left-to-right fashion. In this paper we consider new relations to compute probabilities of partial interpretations of sentences. We introduce theories containing a gap corresponding to an uninterpreted signal segment. Algorithms can be easily obtained from these relations. Computational complexity of these algorithms is also derived.
Anthology ID:
1991.iwpt-1.24
Volume:
Proceedings of the Second International Workshop on Parsing Technologies
Month:
February 13-25
Year:
1991
Address:
Cancun, Mexico
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
210–217
Language:
URL:
https://aclanthology.org/1991.iwpt-1.24
DOI:
Bibkey:
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PDF:
https://aclanthology.org/1991.iwpt-1.24.pdf