An Approach to the POS Tagging Problem Using Genetic Algorithms

Abstract

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.

Comparison of Stochastic and Rule-Based POS Tagging on Malay Online Text

“…Currently the available approaches for POS tagging are based on either stochastic or ruled based methods (Silva et al, 2013) to tag unknown word or disambiguate word. Stochastic taggers utilized maximum likelihood of a word in the sentence.…”

Annotation and Classification of Argumentative Writing Revisions

“…The idea of sequence mutation is introduced from genetic algorithms to generate possible sentence alignment corrections. There are works on tagging problems (Araujo, 2002;Alba et al, 2006;Silva et al, 2013) where genetic algorithms are applied to learn a best labeling or rules for labeling. However, our approach does not follow the standard genetic algorithm in that we do not have crossover operations and we stop mutating when the current generation is worse than last.…”

Disambiguating the Twitter Stream Entities and Enhancing the Search Operation Using DBpedia Ontology

“…Given the tweet streams of the specified event, the task is to extract the potential named entity phrases and filter the typed dependency relationships between the named entities and other related mentions in the tweets. Being consider this as an upheaval task, we have slightly modified the Standard POS Tagger algorithm (Ana Paula Silva et al, 2013) and proposed the new algorithm according to the requirement of our extraction task. In the Standard POS Tagger algorithm, the parser splits the sentences and collects the Noun Phrases, Verb Phrases etc and gives the appropriate labeling for all the segregated tokens.…”