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In case of forward search, a rule corresponding to a right regular grammar is constructed (cf. Section 3.2).
The state machine representation restricts us to the subset of rules that may be expressed by a regular grammar, e.g. a rule may not reference itself.
The program is based around the use of a regular grammar to guide tokenisation and parsing of chemical names, followed by step-wise application of nomenclature rules.
Our current implementation allows regular grammar rules.
In this paper we presented a new class of conditional random fields that assigns labels in agreement with production rules of a defined regular grammar.
The states of the automaton are the non-terminal symbols of the regular grammar and the arrows represent the allowed transitions (or production rules).
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One possible solution is to substitute regular grammars by graph grammars.
The main contribution of the paper is the application of stochastic regular grammars, with learning capability, to generate the robots team's language.
State merging algorithms have emerged as the solution of choice for the problem of inferring regular grammars from labeled samples, a known NP-complete problem of great importance in the grammatical inference area.
That is, even stimulus sets that could be captured by regular grammars tend to be captured using supraregular grammars.
Stochastic context-free grammars provide access to such models, just as stochastic regular grammars (in the form of profile HMMs) can be used to model protein families.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com