Portuguese Part-of-Speech Tagging Using Entropy Guided Transformation Learning

Santos, Cícero Nogueira dos; Milidiú, Ruy Luiz; Rentería, Raúl P.

doi:10.1007/978-3-540-85980-2_15

Cited by 18 publications

(2 citation statements)

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“…Most of the stochastic taggers are based on hidden Markov models. In the other group, we find rule based taggers (Brill (1995); Wilson and Heywood (2005); Nogueira Dos Santos et al (2008)). The rules are usually discovered automatically, and its purpose is to correct errors resulting from an initial basic tagging.…”

Section: Introductionmentioning

confidence: 99%

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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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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%