Fast string correction with Levenshtein automata

Schulz, Klaus U.; Mihov, Stoyan

doi:10.1007/s10032-002-0082-8

Cited by 140 publications

(81 citation statements)

References 28 publications

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“…The earliest approaches presented an algorithmic way to implement the finite-state network traversal with error-tolerance [15] in a fast and effective manner [16,17]. Schulz and Mihov [18] presented the LevenshteinDamerau distance in a finite-state form such that the finite-state spelling correction could be performed using standard finite-state algebraic operations with any existing finite-state library. Furthermore, e.g., Pirinen and Lindén [19] have shown that the weighted finite-state methods can be used to gain the same expressive power as the existing statistical spellchecking software algorithms.…”

Section: A Brief History Of Automatic Spell-checking and Correctionmentioning

confidence: 99%

“…As the finite-state formulations of error models are the most recent devel-opment in finite-state spell-checking, the earliest reference to a finite-state error mod-el in an actual spell-checking system is by Schulz and Mihov [18]. It also contains a very thorough description of building finite-state models for different edit distances.…”

Section: Compiling Finite-state Versions Of Error Modelsmentioning

confidence: 99%

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State-of-the-Art in Weighted Finite-State Spell-Checking

Pirinen

Lindén

2014

Computational Linguistics and Intelligent Text Processing

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The following claims can be made about finite-state methods for spell-checking: 1) Finite-state language models provide support for morphologically complex languages that word lists, affix stripping and similar approaches do not provide; 2) Weighted finite-state models have expressive power equal to other, state-of-the-art string algorithms used by contemporary spell-checkers; and 3) Finite-state models are at least as fast as other string algorithms for lookup and error correction. In this article, we use some contemporary non-finite-state spell-checking methods as a baseline and perform tests in light of the claims, to evaluate state-of-the-art finitestate spell-checking methods. We verify that finite-state spell-checking systems outperform the traditional approaches for English. We also show that the models for morphologically complex languages can be made to perform on par with English systems.

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Section: A Brief History Of Automatic Spell-checking and Correctionmentioning

confidence: 99%

Section: Compiling Finite-state Versions Of Error Modelsmentioning

confidence: 99%

State-of-the-Art in Weighted Finite-State Spell-Checking

Pirinen

Lindén

2014

Computational Linguistics and Intelligent Text Processing

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“…The choice of the value n yields a compromise between recall and computational efficiency. From an algorithmic point of view, techniques described in [14] are used to preselect correction candidates, cf. Section 7.…”

Section: Step 2 Of Phase 1: Computation Of Correction Filesmentioning

confidence: 99%

A visual and interactive tool for optimizing lexical postcorrection of OCR results

Strohmaier

Ringlstetter²,

Schulz³

et al. 2003

2003 Conference on Computer Vision and Pattern Recognition Workshop

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“…Balls of strings, Levenshtein automata and other finite state machines linking regular languages and the edit distance have been introduced, discussed and studied [17,4,16,23].…”

Section: Introductionmentioning

confidence: 99%

Computing the Expected Edit Distance from a String to a Probabilistic Finite-State Automaton

Calvo-Zaragoza

Oncina

Higuera

2017

Int. J. Found. Comput. Sci.

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Received (Day Month Year) Accepted (Day Month Year) Communicated by (xxxxxxxxxx)In a number of fields, it is necessary to compare a witness string with a distribution. One possibility is to compute the probability of the string for that distribution. Another, giving a more global view, is to compute the expected edit distance from a string randomly drawn to the witness string. This number is often used to measure the performance of a prediction, the goal then being to return the median string, or the string with smallest expected distance. To be able to measure this, computing the distance between a hypothesis and that distribution is necessary. This paper proposes two solutions for computing this value, when the distribution is defined with a probabilistic finite state automaton. The first is exact but has a cost which can be exponential in the length of the input string, whereas the second is a fully polynomial-time randomized schema.

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Fast string correction with Levenshtein automata

Cited by 140 publications

References 28 publications

State-of-the-Art in Weighted Finite-State Spell-Checking

State-of-the-Art in Weighted Finite-State Spell-Checking

A visual and interactive tool for optimizing lexical postcorrection of OCR results

Computing the Expected Edit Distance from a String to a Probabilistic Finite-State Automaton

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