Stochastic grammar
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A stochastic grammar (statistical grammar) is a grammar framework with a probabilistic notion of grammaticality:
 Stochastic contextfree grammar
 Statistical parsing
 Dataoriented parsing
 Hidden Markov model
 Estimation theory
The grammar is realized as a language model. Allowed sentences are stored in a database together with the frequency how common a sentence is.^{[1]} Statistical natural language processing uses stochastic, probabilistic and statistical methods, especially to resolve difficulties that 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. "A probabilistic model consists of a nonprobabilistic model plus some numerical quantities; it is not true that probabilistic models are inherently simpler or less structural than nonprobabilistic models."^{[2]}
Examples
A probabilistic method for rhyme detection is implemented by Hirjee & Brown^{[3]} in their study in 2013 to find internal and imperfect rhyme pairs in rap lyrics. The concept is adapted from a sequence alignment technique using BLOSUM (BLOcks SUbstitution Matrix). They were able to detect rhymes undetectable by nonprobabilistic models.
See also
 Colorless green ideas sleep furiously
 Computational linguistics
 Lsystem#Stochastic grammars
 Stochastic contextfree grammar
 Statistical language acquisition
References
 ^ Steve Young; Gerrit Bloothooft (14 March 2013). CorpusBased Methods in Language and Speech Processing. Springer Science & Business Media. pp. 140–. ISBN 9789401711838.
 ^ John Goldsmith. 2002. "Probabilistic Models of Grammar: Phonology as Information Minimization." Phonological Studies #5: 21–46.
 ^ Hirjee, Hussein; Brown, Daniel (2013). "Using Automated Rhyme Detection to Characterize Rhyming Style in Rap Music" (PDF). Empirical Musicology Review.
Further reading
 Christopher D. Manning, Hinrich Schütze: Foundations of Statistical Natural Language Processing, MIT Press (1999), ISBN 9780262133609.
 Stefan Wermter, Ellen Riloff, Gabriele Scheler (eds.): Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing, Springer (1996), ISBN 9783540609254.
 Pirani, Giancarlo, ed. Advanced algorithms and architectures for speech understanding. Vol. 1. Springer Science & Business Media, 2013.
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