Extracting Subregular constraints from Regular stringsets

Rogers, John H.; Lambert, Dakotah

doi:10.15398/jlm.v7i2.209

Cited by 7 publications

(9 citation statements)

References 16 publications

(15 reference statements)

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“…Concepts from finite model theory provide a uniform way to describe relational structures and their parts in logical terms (see Libkin 2004 for a thorough introduction). Applying these concepts to linguistic structure is not a new idea, with applications to syntax by Rogers (1996Rogers ( , 1998 beginning to popularize the approach. In this section we discuss a model-theoretic treatment of relativized adjacency.…”

Section: Model Theorymentioning

confidence: 99%

Relativized Adjacency

Lambert

2023

J of Log Lang and Inf

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Model Theorymentioning

confidence: 99%

Relativized Adjacency

Lambert

2023

J of Log Lang and Inf

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“…An efficient algorithm for deciding SL is described by Edlefsen et al (2008). Algorithms that extract SL and SP factors from a given language (and thus can also be used to decide class membership) are due to Rogers and Lambert (2019a), and these were extended to the TSL class by Lambert and Rogers (2020).…”

Section: Further Readingmentioning

confidence: 99%

Typology emerges from simplicity in representations and learning

Lambert

Rawski

Heinz

2021

JLM

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We derive well-understood and well-studied subregular classes of formal languages purely from the computational perspective of algorithmic learning problems. We parameterise the learning problem along dimensions of representation and inference strategy. Of special interest are those classes of languages whose learning algorithms are necessarily not prohibitively expensive in space and time, since learners are often exposed to adverse conditions and sparse data. Learned natural language patterns are expected to be most like the patterns in these classes, an expectation supported by previous typological and linguistic research in phonology. A second result is that the learning algorithms presented here are completely agnostic to choice of linguistic representation. In the case of the subregular classes, the results fall out from traditional model-theoretic treatments of words and strings. The same learning algorithms, however, can be applied to model-theoretic treatments of other linguistic representations such as syntactic trees or autosegmental graphs, which opens a useful direction for future research.

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“…Béal et al [10] describe a quadratictime algorithm to compute the set of minimal forbidden words of a factorial regular language. Rogers and Lambert [11,12] present algorithms for extracting the shortest forbidden factors and suffixes from a local DFA, which arose from a practical need in their research of analyzing stress patterns in human languages. They also mention the possibility of extracting the shortest forbidden prefixes by extracting suffixes from a DFA accepting reversed language.…”

Section: Introductionmentioning

confidence: 99%

Shortest Characteristic Factors of a Deterministic Finite Automaton and Computing Its Positive Position Run by Pattern Set Matching

Janoušek,

Plachý

2024

Preprint

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Given a deterministic finite automaton (DFA) A, we present a simple algorithm for constructing deterministic finite automata that accept the shortest forbidden factors, the shortest forbidden prefixes, the shortest forbidden suffixes, the shortest forbidden words, the shortest allowed suffixes, and the shortest allowed words of the automaton A. We refer to these sets as the shortest characteristic factors of the automaton A. If the given automaton is local, and therefore the language it accepts is strictly locally testable, the sets of its shortest characteristic factors are finite, and these automata are acyclic. Otherwise, they accept infinite languages. This approach simplifies existing methods for the extraction of forbidden factors, allows the extraction of more types of characteristic factors, and also generalizes the extraction for all classes of DFAs. Furthermore, we demonstrate that this type of extraction can be used for a sublinear run of an automaton for certain inputs. We define a positive position run of a deterministic finite automaton, representing all positions in an input string where the automaton reaches a final state. Finally, we present an algorithm for computing the positive position run of the automaton, which utilizes pattern set matching of its shortest forbidden factors and its shortest forbidden or allowed suffixes, provided that the sets are finite. We showcase the computation of the positive position run of a local automaton using backward pattern set matching, which can achieve sublinear time.

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Extracting Subregular constraints from Regular stringsets

Cited by 7 publications

References 16 publications

Relativized Adjacency

Relativized Adjacency

Typology emerges from simplicity in representations and learning

Shortest Characteristic Factors of a Deterministic Finite Automaton and Computing Its Positive Position Run by Pattern Set Matching

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