Part-of-Speech Tagging with Evolutionary Algorithms

Araujo, Lourdes

doi:10.1007/3-540-45715-1_21

Cited by 26 publications

(24 citation statements)

References 5 publications

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“…Araujo [1,2] has developed a couple of GA implementations that yield impressive accuracies of up to approximately 95% [1] and a mean of up 96.3% [2] on his chosen corpus (Brown corpus). His first implementation [1] did use a GA for POS tagging, but it differed from the Brill GA hybrid proposed here in a number of respects.…”

Section: Evolutionary Pos Taggersmentioning

confidence: 99%

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Use of a genetic algorithm in brill's transformation-based part-of-speech tagger

Wilson

Heywood

2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

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The tagging problem in natural language processing is to find a way to label every word in a text as a particular part of speech, e.g., proper noun. An effective way of solving this problem with high accuracy is the transformation-based or "Brill" tagger. In Brill's system, a number of transformation templates are specified a priori that are instantiated and ranked during a greedy searchbased algorithm. This paper describes a variant of Brill's implementation that instead uses a genetic algorithm to generate the instantiated rules and provide an adaptive ranking. Based on tagging accuracy, the new system provides a better hybrid evolutionary computation solution to the part-of-speech (POS) problem than the previous attempt. Although not able to make up for the use of a priori knowledge utilized by Brill, the method appears to point the way for an improved solution to the tagging problem.

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Section: Evolutionary Pos Taggersmentioning

confidence: 99%

“…His first implementation [1] did use a GA for POS tagging, but it differed from the Brill GA hybrid proposed here in a number of respects. The structure of the individual was markedly different: each gene in an individual was simply a tag with probabilities of different associated contexts attached to it.…”

Section: Evolutionary Pos Taggersmentioning

confidence: 99%

Use of a genetic algorithm in brill's transformation-based part-of-speech tagger

Wilson

Heywood

2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

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“…These approaches can also be divided by the type of information used to solve the problem, statistical information [3,4,5,6,7], and rule-based information [9]. Shortly, in the former, an evolutionary algorithm is used to assign the most likely tag to each word of a sentence, based on a context table, that basically has the same information that is used in the traditional probabilistic approaches.…”

Section: Introductionmentioning

confidence: 99%

“…The first group is based on statistical data concerning the different context possibilities for a word [2,3,4,5,6,7], while the second group is based on rules, normally designed by human experts, that capture the language properties [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

An Approach to the POS Tagging Problem Using Genetic Algorithms

Silva

Rodrigues

2015

Studies in Computational Intelligence

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Abstract. The automatic part-of-speech tagging is the process of automatically assigning to the words of a text a part-of-speech (POS) tag. The words of a language are grouped into grammatical categories that represent the function that they might have in a sentence. These grammatical classes (or categories) are usually called part-of-speech. However, in most languages, there are a large number of words that can be used in different ways, thus having more than one possible part-of-speech. To choose the right tag for a particular word, a POS tagger must consider the surrounding words' part-of-speeches. The neighboring words could also have more than one possible way to be tagged. This means that, in order to solve the problem, we need a method to disambiguate a word's possible tags set. In this work, we modeled the part-of-speech tagging problem as a combinatorial optimization problem, which we solve using a genetic algorithm. The search for the best combinatorial solution is guided by a set of disambiguation rules that we first discovered using a classification algorithm, that also includes a genetic algorithm. Using rules to disambiguate the tagging, we were able to generalize the context information present on the training tables adopted by approaches based on probabilistic data. We were also able to incorporate other type of information that helps to identify a word's grammatical class. The results obtained on two different corpora are amongst the best ones published.

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“…Genetic algorithms have been previously applied to the problem [3,4], obtaining accuracies as good of those of typical algorithms used for stochastic tagging (such as the widely used of Viterbi [5]) or even better [4]. CHC is a non-traditional genetic algorithm, which presents some particular features.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics for Natural Language Tagging

Araujo

Luque

Alba

2004

Genetic and Evolutionary Computation – GECCO 2004

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Abstract. This work compares different metaheuristics techniques applied to an important problem in natural language: tagging. Tagging amounts to assigning to each word in a text one of its possible lexical categories (tags) according to the context in which the word is used (thus it is a disambiguation task). Specifically, we have applied a classic genetic algorithm (GA), a CHC algorithm, and a Simulated Annealing (SA). The aim of the work is to determine which one is the most accurate algorithm (GA, CHC or SA), which one is the most appropriate encoding for the problem (integer or binary) and also to study the impact of parallelism on each considered method. The work has been highly simplified by the use of MALLBA, a library of search techniques which provides generic optimization software skeletons able to run in sequential, LAN and WAN environments. Experiments show that the GA with the integer encoding provides the more accurate results. For the CHC algorithm, the best results are obtained with binary coding and a parallel implementation. SA provides less accurate results than any of the evolutionary algorithms.

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Part-of-Speech Tagging with Evolutionary Algorithms

Cited by 26 publications

References 5 publications

Use of a genetic algorithm in brill's transformation-based part-of-speech tagger

Use of a genetic algorithm in brill's transformation-based part-of-speech tagger

An Approach to the POS Tagging Problem Using Genetic Algorithms

Metaheuristics for Natural Language Tagging

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