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An Introduction to Machine Translation
, 1992
"... Abstract. In the last ten years there has been a significant amount of research in Machine Translation within a “new ” paradigm of empirical approaches, often labelled collectively as “Examplebased” approaches. The first manifestation of this approach caused some surprise and hostility among observ ..."
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Cited by 407 (9 self)
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Abstract. In the last ten years there has been a significant amount of research in Machine Translation within a “new ” paradigm of empirical approaches, often labelled collectively as “Examplebased” approaches. The first manifestation of this approach caused some surprise and hostility among observers more used to different ways of working, but the techniques were quickly adopted and adapted by many researchers, often creating hybrid systems. This paper reviews the various research efforts within this paradigm reported to date, and attempts a categorisation of different manifestations of the general approach.
Serial Computations of Levenshtein Distances
, 1997
"... sequence (LCS) of those strings. If D is the simple Levenshtein distance between two strings having lengths m and n, SES is the length of the shortest edit sequence between the strings, and L is the length of an LCS of the strings, then SES = D and L = (m + n 0D)=2. We will focus on the problem of ..."
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Cited by 19 (0 self)
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sequence (LCS) of those strings. If D is the simple Levenshtein distance between two strings having lengths m and n, SES is the length of the shortest edit sequence between the strings, and L is the length of an LCS of the strings, then SES = D and L = (m + n 0D)=2. We will focus on the problem of determining the length of an LCS and also on the related problem of recovering an LCS. Another related problem, which will be discussed in Chapter 7, is that of approximate string matching, in which it is desired to locate all positions within string y which begin an approximation to string x containing at most D errors (insertions or deletions). 124 SERIAL COMPUTATIONS OF LEVENSHTEIN DISTANCES procedure CLASSIC( x,<
Frequency in Morphology
"... Introduction The recent work in statistical parsing (Church 1988, Schabes 1991) and statistical machine translation (Brown et al 1990) calls the traditional rulebased view of grammar into question. These authors emphasize that grammatical rule systems aiming at syntaxdirected translation, and eve ..."
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Cited by 3 (2 self)
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Introduction The recent work in statistical parsing (Church 1988, Schabes 1991) and statistical machine translation (Brown et al 1990) calls the traditional rulebased view of grammar into question. These authors emphasize that grammatical rule systems aiming at syntaxdirected translation, and even rule systems aimed at the description of a single language, break down when faced with the actual complexity of natural language data. In fact, under realistic testing conditions the "examplebased" or "corpusbased " systems that employ some generalpurpose optimization algorithm in order to extract statistical regularities from the data fare just as well as the rulebased systems in which the regularities are extracted beforehand by the grammarian. In the light of these facts it is natural to extend the inquiry to morphology and ask how statistical morphological systems that exploit the frequency information in the data will compare with rulebased morphological sy
A Hybrid Genetic Algorithm for RNA Structural Alignment
"... The RNA structural alignment is one of the most challenging tasks in bioinformatics. However, finding the accurate conserved structure of a set of RNA sequences is still being a difficult task. In this work, the problem is cast as an optimization problem for which a new framework relaying on hybrid ..."
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The RNA structural alignment is one of the most challenging tasks in bioinformatics. However, finding the accurate conserved structure of a set of RNA sequences is still being a difficult task. In this work, the problem is cast as an optimization problem for which a new framework relaying on hybrid genetic algorithm is proposed. The contribution consists in using a new objective function based on the Structure Conservation Index (SCI). In order to enhance the Genetic Algorithms (GA) performances, a Simulated Annealing (SA) procedure has been used. The proposed algorithm is composed on two phases.The first phase consists of applying a genetic algorithm.In the second phase, the simulated annealing procedure is applied in order to improve the final population given by the genetic algorithm. Experiments on a wide range of data sets have shown the effectiveness of the proposed framework and its ability to achieve good quality solutions comparing to those given by others techniques.
GER FI JSYRH EX (3 < Using Genetic Algorithms to Create Meaningful Poetic Text
"... This paper presents a series of experiments in automatically generating poetic texts. We confined our attention to the generation of texts which are syntactically wellformed, meet certain prespecified patterns of metre, and broadly convey some given meaning. Such aspects can be formally defined, t ..."
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This paper presents a series of experiments in automatically generating poetic texts. We confined our attention to the generation of texts which are syntactically wellformed, meet certain prespecified patterns of metre, and broadly convey some given meaning. Such aspects can be formally defined, thus avoiding the complications of imagery and interpretation that are central to assessing more free forms of verse. Our implemented system, McGonagall, applies the genetic algorithm to construct such texts. It uses a sophisticated linguistic formalism to represent its genomic information, from which can be computed the phenotypic information of both semantic representations and patterns of stress. The conducted experiments broadly indicated that relatively meaningful text could be produced if the constraints on metre were relaxed, and precise metric text was possible with loose semantic constraints, but it was difficult to produce text which was both semantically coherent and of high quality metrically.
“Introduction ” to the Handbook of Organization Studies
, 1998
"... Copyright. All rights reserved. Not to be quoted, paraphrased, copied, or distributed in any fashion. * I wish to give special thanks to Elaine Mosakowski for her contributions to this paper. All remaining errors are my responsibility. ..."
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Copyright. All rights reserved. Not to be quoted, paraphrased, copied, or distributed in any fashion. * I wish to give special thanks to Elaine Mosakowski for her contributions to this paper. All remaining errors are my responsibility.
DESCRIPTION OF THE SYNTAGMATIC PARADIGMATIC MODEL............................................ 7
"... The Syntagmatic Paradigmatic (SP) model is a distributed, memorybased account of verbal processing. Built on a Bayesian interpretation of string edit theory, it characterizes the control of verbal cognition as the retrieval of sets of syntagmatic and paradigmatic constraints from sequential and rel ..."
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The Syntagmatic Paradigmatic (SP) model is a distributed, memorybased account of verbal processing. Built on a Bayesian interpretation of string edit theory, it characterizes the control of verbal cognition as the retrieval of sets of syntagmatic and paradigmatic constraints from sequential and relational longterm memory and the resolution of these constraints in working memory. Lexical information is extracted directly from text using a version of the Expectation Maximization (EM) algorithm. In this paper, the model is described and then illustrated on a number of phenomena including sentence processing, semantic categorization and rating, short term serial recall and analogical and logical inference. Subsequently, the model is applied to a large scale corpus and used to extract syntactic structure and to assign syntactic
4 Serial Computations of Levenshtein Distances
"... In the previous chapters, we discussed problems involving an exact match of string patterns. We now turn to problems involving similar but not necessarily exact pattern matches. There are a number of similarity or distance measures, and many of them are special cases or generalizations of the Levens ..."
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In the previous chapters, we discussed problems involving an exact match of string patterns. We now turn to problems involving similar but not necessarily exact pattern matches. There are a number of similarity or distance measures, and many of them are special cases or generalizations of the Levenshtein metric. The problem of evaluating the measure of string similarity has numerous applications, including one arising in the study of the evolution of long molecules such as proteins. In this chapter, we focus on the problem of evaluating a longest common subsequence, which is expressively equivalent to the simple form of the Levenshtein distance. 4.1 Levenshtein distance and the LCS problem The Levenshtein distance is a metric that measures the similarity of two strings. In its simple form, the Levenshtein distance, D(x; y), between strings x and y is the minimum number of character insertions and/or deletions (indels) required to transform string x into string y. A commonly used generalization of the Levenshtein distance is the minimum cost of transforming x into y when the allowable operations are character insertion, deletion, and substitution, with costs ( ;); ( ;), and ( 1; 2), that are functions of the involved character(s). There are direct correspondences between the Levenshtein distance of two strings, the length of the shortest edit sequence from one string to the other, and the length of the longest common subsequence (LCS) of those strings. If D is the simple Levenshtein distance between two strings having lengths m and n, SES is the length of the shortest edit sequence between the strings, and L is the length of an LCS of the strings, then SES = D and L = (m + n 0 D)=2. We will focus on the problem of determining the length of an LCS and also on the related problem of recovering an LCS. Another related problem, which will be discussed in Chapter 7, is that of approximate string matching, in which it is desired to locate all positions within string y which begin an approximation to string x containing at most D errors (insertions or deletions).124 SERIAL COMPUTATIONS OF LEVENSHTEIN DISTANCES procedure CLASSIC ( x, m, y, n, C, p): begin