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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 ...
One way to do this is by using a probabilistic context-free grammar (PCFG) which has a probability of each constituency rule, and modifying CKY to maximise probabilities when parsing bottom-up. [6] [7] [8] A further modification is the lexicalized PCFG, which assigns a head to each constituent and encodes rule for each lexeme in that head slot.
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.
Probabilistic logic programming is a programming paradigm that extends logic programming with probabilities. Most approaches to probabilistic logic programming are based on the distribution semantics, which splits a program into a set of probabilistic facts and a logic program. It defines a probability distribution on interpretations of the ...
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.
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 ...
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