stochastic grammar
A stochastic grammar (statistical grammar) is a grammar framework with a probabilistic notion of grammaticality:
- Stochastic context-free grammar
- Statistical parsing
- Data-oriented parsing
- Hidden Markov model
- Estimation theory
Statistical natural language processing uses stochastic, probabilistic and statistical methods, especially to resolve difficulties which arise because longer sentences are highly ambiguous when processed with realistic grammars, yielding thousands or millions of possible analyses. Methods for disambiguation often involve the use of corpora and Markov models. The technology for statistical NLP comes mainly from machine learning and data mining, both of which are fields of artificial intelligence that involve learning from data.
Literature
- Christopher D. Manning, Foundations of Statistical Natural Language Programming, MIT Press (1999), ISBN 978-0262133609.
- Stefan Wermter, Ellen Riloff, Gabriele Scheler (eds.) Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing, Springer (1996), ISBN 978-3540609254.
See also
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