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Implicit learning of recursive context-free grammars. [PDF]
Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed
Martin Rohrmeier+2 more
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LPG–PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords [PDF]
With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password ...
Xiaozhou Guo+4 more
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Learning of Structurally Unambiguous Probabilistic Grammars [PDF]
The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule.
Dana Fisman+2 more
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Learning Cover Context-Free Grammars from Structural Data [PDF]
We consider the problem of learning an unknown context-free gram- mar from its structural descriptions with depth at most ℓ. The structural descriptions of the context-free grammar are its unlabelled derivation trees. The goal is to learn a cover context-
M. Marin, G. Istrate
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A stochastic context free grammar based framework for analysis of protein sequences [PDF]
Background In the last decade, there have been many applications of formal language theory in bioinformatics such as RNA structure prediction and detection of patterns in DNA.
Nebel Jean-Christophe, Dyrka Witold
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A universal context-free grammar [PDF]
In this report we show that, for each alphabet Σ, there exists a context-free grammar G which satisfies the property that for each context-free language L ⊆ Σ* a regular control set C can be found such that LC(G ...
Kasai, Takumi
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On a property of probabilistic context-free grammars [PDF]
It is proved that for a probabilistic context‐free language L(G), the population density of a character (terminal symbol) is equal to its relative density in the words of a sample S from L(G) whenever the production probabilities of the grammar G are estimated by the relative frequencies of the corresponding productions in the sample.
Ranjan Chaudhuri, A.N.V. Rao
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Reduction of context-free grammars [PDF]
This paper is concerned with the following problem: Given a context-free grammar G, find a context-free grammar with the fewest nonterminal symbols or with the fewest rules that is equivalent to G.
Kasami, Tadao, Taniguchi, Kenichi
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Translations on a context free grammar [PDF]
Two schemes for the specification of translations on a context-free grammar are proposed. The first scheme, called a generalized syntax directed translation (GSDT), consists of a context free grammar with a set of semantic rules associated with each ...
Aho, A.V., Ullman, J.D.
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Complexity of Problems of Commutative Grammars [PDF]
We consider commutative regular and context-free grammars, or, in other words, Parikh images of regular and context-free languages. By using linear algebra and a branching analog of the classic Euler theorem, we show that, under an assumption that the ...
Eryk Kopczynski
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