Automatic Morpheme Slot Identification Using Genetic Algorithm View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Wondwossen Mulugeta , Michael Gasser , Baye Yimam

ABSTRACT

We introduce an approach to the grouping of morphemes into suffix slots in morphologically complex languages using genetic algorithm. The method is applied to verbs in Amharic, an under-resourced morphologically rich Semitic language, with a number of non-concatenative prefix and suffix morphemes. We start with a limited set of segmented verbs and the set of suffixes themselves, extracted on the basis of our previous work. Each member of the population for the genetic algorithm is an assignment of the morphemes to one of the possible slots. The fitness function combines scores for exact slot position and correct ordering of morphemes. We use mutation but no crossover operator with various combinations of population size, mutation rate, and number of generations, and models evolve to yield promising morpheme classification results with 90.02 % accuracy level. We evaluate the fittest individuals on the basis of the known morpheme classes for Amharic. More... »

PAGES

85-97

References to SciGraph publications

Book

TITLE

Human Language Technology. Challenges for Computer Science and Linguistics

ISBN

978-3-319-43807-8
978-3-319-43808-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-43808-5_7

DOI

http://dx.doi.org/10.1007/978-3-319-43808-5_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011014030


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