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 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."
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.
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Famous quotes containing the word grammar:
“Grammar is a tricky, inconsistent thing. Being the backbone of speech and writing, it should, we think, be eminently logical, make perfect sense, like the human skeleton. But, of course, the skeleton is arbitrary, too. Why twelve pairs of ribs rather than eleven or thirteen? Why thirty-two teeth? It has something to do with evolution and functionalism—but only sometimes, not always. So there are aspects of grammar that make good, logical sense, and others that do not.”
—John Simon (b. 1925)