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Abstract: This article presents a combination of unsupervised and supervised learning techniques for the generation of word segmentation rules from a raw list of words. First, a language bias for word segmentation is introduced and a simple genetic algorithm is used in the search for segmentation that corresponds to the best bias value. In the second phase, the words segmented by the genetic algorithm are used as an input for the first order decision list learner Clog. The result is a set of first ... (Update)
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.... induction, robust parsing, anaphora resolution and morphological analysis [Losee 1995] Ros 1998] Orasan et al. 2000] [Kazakov and Manandhar 2000]. To the author s knowledge there has not been any report on using GA based techniques to extract bilingual dictionaries. 2.1...
...their correct classification. Later on, learning can be used to replace these exceptions with rules, if possible. Foidl [24] and Clog [15] are two of the first order decision list learners. It is also worth mentioning that Clog, unlike Progol, is an incremental learner. Eager...
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BibTeX entry: (Update)
Kazakov, K. and Manandhar, S. (2000) Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming. To appear in Journal of Machine Learning. http://citeseer.ist.psu.edu/kazakov00unsupervised.html More
@article{ kazakov01unsupervised,
author = "Dimitar Kazakov and Suresh Manandhar",
title = "Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming",
journal = "Machine Learning",
volume = "43",
number = "1/2",
pages = "121-162",
year = "2001",
url = "citeseer.ist.psu.edu/kazakov00unsupervised.html" }
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