Kazakov, Dimitar; and Suresh Manandhar. 2001. "Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming". Machine Learning 43:121-162. Home/Search Document Details and Download
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Evolutionary Sentence Building for Chatterbots - Vrajitoru (Correct)
....new sentences based on the ones retrieved from the database and enhance the diversity of response of the chatterbot. Evolutionary computation is not a very common method for natural language processing. Some application of the genetic algorithms to this eld include adaptive word segmentation [8], grammar learning [3] linguistic classi cation [13] and information retrieval [5] 17] 4.1 OUR MODEL The combination of the previous methods may seem enough to generate interesting and diverse conversations with a human opponent. We would still like the system to be able to learn new ....
D. Kazakov and S. Manandhar. Unsupervised learning of word segmentation rules with genetic algorithms and inductive logic programming. Machine Learning, 43:121-162, 2001.
Building a Bilingual Dictionary with Scarce Resources: A Genetic.. - Han (2000) (Correct)
....yield a better translation [Goldberg 1989] Until recently GA has not been widely used in the field of computational linguistics. Several works have been reported for grammar 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 Algorithm Outline The algorithm applies various GA operators on a population of solutions to maximize the objective function S(f) A solution (translation mapping) is ....
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.
Machine Learning and Inductive Logic Programming for.. - Kazakov, Kudenko (2001) Self-citation (Kazakov) (Correct)
....acquired training examples. If the examples are misclassified by the existing theory, they can be added as exceptions at the top of the decision list to ensure 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 ILP vs. Analogical Prediction The relative merits of eager and lazy learning have already been discussed. ILP belongs to the eager learning paradigm, with the ....
Dimitar Kazakov and Suresh Manandhar. Unsupervised learning of word segmentation rules with genetic algorithms and inductive logic programming. Machine Learning, 2001. Forthcomming.
Resources for Morphology Learning and - Evaluation Mike Maxwell (2002) (Correct)
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Kazakov, Dimitar; and Suresh Manandhar. 2001. "Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming". Machine Learning 43:121-162.
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D. Kazakov and S. Manandhar. Unsupervised learning of word segmentation rules with genetic algorithms and inductive logic programming. Machine Learning, 43:121--162, 2001.
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