Abstract
This paper presents a part-of-speech tagger based on a genetic algorithm which, after the “evolution” of a population of sequences of tags for the words in the text, selects the best individual as solution. The paper describes the main issues arising in the algorithm, such as the chromosome representation and the evaluation and design of genetic operators for crossover and mutation. A probabilistic model, based on the context of each word (the tags of the surrounding words) has been devised in order to define the fitness function. The model has been implemented and different issues have been investigated: size of the training corpus, effect of the context size, and parameters of the evolutionary algorithm, such as population size and crossover and mutation rates. The accuracy obtained with this method is comparable to that of other probabilistic approaches, but evolutionary algorithms are more efficient in obtaining the results.
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Araujo, L. (2002). Part-of-Speech Tagging with Evolutionary Algorithms. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2002. Lecture Notes in Computer Science, vol 2276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45715-1_21
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DOI: https://doi.org/10.1007/3-540-45715-1_21
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