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Grammar induction
Grammar induction, also known as grammatical inference or syntactic pattern recognition, refers to 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. More generally, grammatical inference is that branch of machine learning where the instance space consists of discrete combinatorial objects such as strings, trees and graphs.
==Grammar classes==
Grammatical inference has often been very focused on the problem of learning finite state machines of various types (see the article Induction of regular languages for details on these approaches), since there have been efficient algorithms for this problem since the 1980s.
More recently these approaches have been extended to the problem of inference of context-free grammars and richer formalisms, such as multiple context-free grammars and parallel multiple context-free grammars.
Other classes of grammars for which grammatical inference has been studied are contextual grammars and pattern languages.
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