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The AI programs first adapted to simulate both natural and artificial grammar learning used the following basic structure: Given A set of grammatical sentences from some language. Find A procedure for recognizing and/or generating all grammatical sentences in that language. An early model for AI grammar learning is Wolff's SNPR System.
Semantics studies the rule systems that determine expressions' meanings. Within generative grammar, semantics is a species of formal semantics, providing compositional models of how the denotations of sentences are computed on the basis of the meanings of the individual morphemes and their syntactic structure. [33]
Some languages use 'n-gram' data, [7] which is massive and requires considerable processing power and I/O speed, for some extra detections. As such, LanguageTool is also offered as a web service that does the processing of 'n-grams' data on the server-side.
Grammar induction (or grammatical inference) [1] is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite-state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects.
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.
The "breadth" of a system is measured by the sizes of its vocabulary and grammar. The "depth" is measured by the degree to which its understanding approximates that of a fluent native speaker. At the narrowest and shallowest, English-like command interpreters require minimal complexity, but have a small range of applications.
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