Unsupervised Learning of Na ve Morphology with Genetic Algorithms
نویسنده
چکیده
The morphological lexicon is an important part of NLP systems which is typ ically hand written with the help of linguist experts Even a partial automation of this process could decrease the cost of the lexicon being of theoretical impor tance for languages and dialects which have not been well analysed yet In this work we describe an attempt to use the minimal description length MDL as the one bias for deriving lexicons of morphemes from a raw list of words MDL is used as a tness function of a simple genetic algorithm Results are reported for a rich morphology language corpus French and future work is discussed
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