Stochastic grammar

A stochastic grammar (statistical grammar) is a grammar framework with a probabilistic notion of grammaticality:

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 non-probabilistic model plus some numerical quantities; it is not true that probabilistic models are inherently simpler or less structural than non-probabilistic models."[1]

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.


A probabilistic method for rhyme detection is implemented by Hirjee & Brown[2] 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 non-probabilistic model.

See also


  1. John Goldsmith. 2002. "Probabilistic Models of Grammar: Phonology as Information Minimization." Phonological Studies #5: 2146.
  2. Hirjee, Hussein; Brown, Daniel (2013). "Using Automated Rhyme Detection to Characterize Rhyming Style in Rap Music" (PDF). Empirical Musicology Review.

Further reading

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