Stochastic grammar

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

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.[citation needed] Some methods for object recognition are based on context-sensitive probabilistic grammars.[2]


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 non-probabilistic models.

See also[edit]


  1. ^ John Goldsmith. 2002. "Probabilistic Models of Grammar: Phonology as Information Minimization." Phonological Studies #5: 21–46.
  2. ^ Zhu, Song-Chun, and David Mumford. "A stochastic grammar of images." Foundations and Trends® in Computer Graphics and Vision 2.4 (2007): 259-362.
  3. ^ Hirjee, Hussein; Brown, Daniel (2013). "Using Automated Rhyme Detection to Characterize Rhyming Style in Rap Music" (PDF). Empirical Musicology Review.

Further reading[edit]

  • Christopher D. Manning, Hinrich Schütze: Foundations of Statistical Natural Language Processing, MIT Press (1999), ISBN 978-0-262-13360-9.
  • Stefan Wermter, Ellen Riloff, Gabriele Scheler (eds.): Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing, Springer (1996), ISBN 978-3-540-60925-4.
  • Pirani, Giancarlo, ed. Advanced algorithms and architectures for speech understanding. Vol. 1. Springer Science & Business Media, 2013.