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• Σ = {C, V};
"... Finite languages are those recognized by rewrite grammars whose production rules are all of the form S → w where w belongs to Σ ∗. In other words, every rule rewrites the start symbol “S ” as a word (or sentence) in the language. ..."
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Finite languages are those recognized by rewrite grammars whose production rules are all of the form S → w where w belongs to Σ ∗. In other words, every rule rewrites the start symbol “S ” as a word (or sentence) in the language.
MaximumLikelihoodEstimationofFeature-basedDistributions
"... Motivated by recent work in phonotactic learning (Hayes and Wilson 2008, Albright 2009), this paper shows how to define feature-based probability distributions whose parameters can be provably efficiently estimated. The main idea is that these distributions are defined as a product of simpler distri ..."
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Motivated by recent work in phonotactic learning (Hayes and Wilson 2008, Albright 2009), this paper shows how to define feature-based probability distributions whose parameters can be provably efficiently estimated. The main idea is that these distributions are defined as a product of simpler distributions (cf. Ghahramani and Jordan 1997). One advantage of this framework is it draws attention to what is minimally necessary to describe and learn phonological feature interactions in phonotactic patterns. The “bottom-up” approach adopted here is contrasted with the “top-down ” approach in Hayes and Wilson (2008), and it is argued that the bottom-up approach is more analytically transparent. 1
bUniversity of Bayreuth
"... On the state complexity of closures and interiors of regular languages with subwords and superwords ..."
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On the state complexity of closures and interiors of regular languages with subwords and superwords
On the State Complexity of the Reverse of R- and J-trivial Regular Languages
"... Abstract. The tight upper bound on the state complexity of the reverse of R-trivial and J-trivial regular languages of the state complexity n is 2n−1. The witness is ternary for R-trivial regular languages and (n − 1)-ary for J-trivial regular languages. In this paper, we prove that the bound can be ..."
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Abstract. The tight upper bound on the state complexity of the reverse of R-trivial and J-trivial regular languages of the state complexity n is 2n−1. The witness is ternary for R-trivial regular languages and (n − 1)-ary for J-trivial regular languages. In this paper, we prove that the bound can be met neither by a binary R-trivial regular language nor by a J-trivial regular language over an (n − 2)-element alphabet. We provide a characterization of tight bounds for R-trivial regular languages depending on the state complexity of the language and the size of its alphabet. We show the tight bound for J-trivial regular languages over an (n − 2)-element alphabet and a few tight bounds for binary J-trivial regular languages. The case of J-trivial regular languages over an (n−k)-element alphabet, for 2 ≤ k ≤ n − 3, is open. 1
Learning Subregular Classes of Languages with Factored Deterministic Automata
"... This paper shows how factored finite-state representations of subregular lan-guage classes are identifiable in the limit from positive data by learners which are polytime iterative and optimal. These rep-resentations are motivated in two ways. First, the size of this representation for a given regul ..."
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This paper shows how factored finite-state representations of subregular lan-guage classes are identifiable in the limit from positive data by learners which are polytime iterative and optimal. These rep-resentations are motivated in two ways. First, the size of this representation for a given regular language can be expo-nentially smaller than the size of the minimal deterministic acceptor recogniz-ing the language. Second, these rep-resentations (including the exponentially smaller ones) describe actual formal lan-guages which successfully model natural language phenomenon, notably in the sub-field of phonology. 1
Longest Common Prefix
, 2014
"... Very efficient learning of structured classes of subsequential functions from ..."
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Very efficient learning of structured classes of subsequential functions from
