Efficient error-correcting Viterbi parsing

Amengual, Juan Carlos; Vidal, Enrique

doi:10.1109/34.722628

Cited by 39 publications

(29 citation statements)

References 15 publications

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“…e.g. [AV98]) are usually employed for computing the maximum likelihood, and not a distribution. Besides their advantage of estimating the edit distance between sequences, these approaches can only be used to match sequences to Markov chains but not two Markov chains.…”

Section: Related Workmentioning

confidence: 99%

Similarity Search on Uncertain Spatio-temporal Data

Niedermayer

Züfle

Emrich

et al. 2013

Similarity Search and Applications

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In this work, we address the problem of similarity search in a database of uncertain spatio-temporal objects. Each object is defined by a set of observations ((time,location)-tuples) and a Markov chain which describes the objects uncertain motion in space and time. To model similarity -which is an important building block for many applications such as identifying frequent motion patterns or trajectory clustering -we employ the well-known Longest Common Subsequence (LCSS) measure, which becomes a distribution on uncertain spatio-temporal data (ULCSS). We show how the aligned version (without time shifting) of the ULCSS can be exactly computed in PTIME, which is also verified by extensive experiments.

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Section: Related Workmentioning

confidence: 99%

Similarity Search on Uncertain Spatio-temporal Data

Niedermayer

Züfle

Emrich

et al. 2013

Similarity Search and Applications

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“…Note that these transitions do not have to be explicitly represented in the expanded FSM. Instead they will be dynamically expanded during the parsing process, taking only the input symbol to be parsed into account (Amengual & Vidal, 1998).…”

Section: Basic Concepts About Error Correctionmentioning

confidence: 99%

“…An efficient Viterbi-like search algorithm which solves the problem posed by equations (1) and (3) (under the max approximation) has recently been developed (Amengual & Vidal, 1998). Full algorithmic details are given in that paper.…”

Section: Basic Concepts About Error Correctionmentioning

confidence: 99%

“…Example of FSM refinement using the ECGRA with a given sequence of six "simplified" learning strings. Amengual (1999), Amengual and Vidal (1998), Gregor and Harris (1995), where parsing algorithms which efficiently solve these problems are proposed. These techniques allow for performing full error-correcting parsing using any kind of FSMs, which are no longer restricted to being acyclic (Amengual, 1999;Amengual & Vidal, 1998).…”

Section: Grammatical Inference Through Error-correctingmentioning

confidence: 99%

“…This approach is reminiscent of the ideas underlying the "Error-Correcting Grammatical Inference" algorithm (ECGI), which were introduced by Vidal (1987, 1988). A recently developed efficient ECP algorithm (Amengual & Vidal, 1998) overcomes the original ECGI limitation of having to work only with acyclic FSMs. On the other hand, the new ECGIlike algorithm includes new heuristics that have specifically been designed to cope with some constructions which often appear in natural language applications.…”

Section: Introductionmentioning

confidence: 99%

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et al. 2001

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Abstract. In many language processing tasks, most of the sentences generally convey rather simple meanings. Moreover, these tasks have a limited semantic domain that can be properly covered with a simple lexicon and a restricted syntax. Nevertheless, casual users are by no means expected to comply with any kind of formal syntactic restrictions due to the inherent "spontaneous" nature of human language. In this work, the use of error-correctingbased learning techniques is proposed to cope with the complex syntactic variability which is generally exhibited by natural language. In our approach, a complex task is modeled in terms of a basic finite state model, F, and a stochastic error model, E. F should account for the basic (syntactic) structures underlying this task, which would convey the meaning. E should account for general vocabulary variations, word disappearance, superfluous words, and so on. Each "natural" user sentence is thus considered as a corrupted version (according to E) of some "simple" sentence of L(F). Adequate bootstrapping procedures are presented that incrementally improve the "structure" of F while estimating the probabilities for the operations of E. These techniques have been applied to a practical task of moderately high syntactic variability, and the results which show the potential of the proposed approach are presented.

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Enhancing Trie-Based Syntactic Pattern Recognition Using AI Heuristic Search Strategies

Badr

Oommen

2005

Pattern Recognition and Data Mining

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Efficient error-correcting Viterbi parsing

Cited by 39 publications

References 15 publications

Similarity Search on Uncertain Spatio-temporal Data

Similarity Search on Uncertain Spatio-temporal Data

Untitled

Enhancing Trie-Based Syntactic Pattern Recognition Using AI Heuristic Search Strategies

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