Abstract
Classic parsing methods use complete search techniques to find the different interpretations of a sentence. However, the size of the search space increases exponentially with the length of the sentence or text to be parsed and the size of the grammar, so that exhaustive search methods can fail to reach a solution in a reasonable time. Nevertheless, large problems can be solved approximately by some kind of stochastic techniques, which do not guarantee the optimum value, but allow adjusting the probability of error by increasing the number of points explored. Evolutionary Algorithms are among such techniques. This paper presents a stochastic chart parser based on an evolutionary algorithm which works with a population of partial parsings. The paper describes the relationships between the elements of a classic chart parser and those of the evolutionary algorithm. The model has been implemented, and the results obtained for texts extracted from the Susanne corpus are presented.
Supported by projects TIC2003-09481-C04 and 07T/0030/2003.
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Araujo, L. (2004). A Probabilistic Chart Parser Implemented with an Evolutionary Algorithm. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2004. Lecture Notes in Computer Science, vol 2945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24630-5_10
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DOI: https://doi.org/10.1007/978-3-540-24630-5_10
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