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Grammar theory to model symbol strings originated from work in computational linguistics aiming to understand the structure of natural languages. [1] [2] [3] Probabilistic context free grammars (PCFGs) have been applied in probabilistic modeling of RNA structures almost 40 years after they were introduced in computational linguistics.
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. The grammar is realized as a language model. Allowed sentences are stored in a database together with the frequency ...
Syntactic parsing is the automatic analysis of syntactic structure of natural language, especially syntactic relations (in dependency grammar) and labelling spans of constituents (in constituency grammar ). [ 1] It is motivated by the problem of structural ambiguity in natural language: a sentence can be assigned multiple grammatical parses, so ...
In computer science, the Cocke–Younger–Kasami algorithm (alternatively called CYK, or CKY) is a parsing algorithm for context-free grammars published by Itiroo Sakai in 1961. [1][2] The algorithm is named after some of its rediscoverers: John Cocke, Daniel Younger, Tadao Kasami, and Jacob T. Schwartz.
A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.
The grammar model discussed in Noam Chomsky's Syntactic Structures (1957) Chomsky's transformational grammar has three parts: phrase structure rules, transformational rules and morphophonemic rules. [68] The phrase structure rules are used for expanding grammatical categories and for substitutions. These yield a string of morphemes. A ...
For parsing algorithms in computer science, the inside–outside algorithm is a way of re-estimating production probabilities in a probabilistic context-free grammar.It was introduced by James K. Baker in 1979 as a generalization of the forward–backward algorithm for parameter estimation on hidden Markov models to stochastic context-free grammars.
v. t. e. A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics —particularly Bayesian statistics —and machine learning.