Abstract
Learning a translation based on a dictionary is to extract a binary relation over strings from given examples based on information supplied by the dictionary. In this paper, we introduce a restricted elementary formal system called a regular TEFS to formalize translations and dictionaries. Then, we propose a learning algorithm that identifies a translation defined by a regular TEFS from positive and negative examples. The main advantage of the learning algorithm is constructive, that is, the produced hypothesis reflects the examples directly. The learning algorithm generates the most specific clauses from examples by referring to a dictionary, generalizes these clauses, and then removes too strong clauses from them. As a result, the algorithm can learn translations over context-free languages.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
S. Arikawa, S. Miyano, A. Shinohara, T. Shinohara, and A. Yamamoto. Algorithmic learning theory with elementary formal systems. IEICE Transaction on Information and Systems., E75-D:405–414, 1992.
S. Arikawa, T. Shinohara, and A. Yamamoto. Learning elementary formal systems. Theoretical Computer Science, 97–113, 1992.
R. B. Banerji. Learning theories in a subset of a polyadic logic. In Proc. Computational Learning Theory '88, 281–295, 1988.
H. Ishizaka. Inductive inference of regular languages based on model inference. International journal of Computer Mathematics, 27:67–83, 1989.
H. Ishizaka. Polynomial time learnability of simple deterministic languages. Machine Learning, 5(2):151–164, 1990.
X. Ling. Learning and invention of Horn clause theories — a constructive method. In Z. W. Ras, editor, Methodologies for Intelligent System, 4:323–331, NorthHolland, 1989.
S. Muggleton. Inductive logic programming. Academic Press, 1992.
S. Muggleton and W. Buntine. Machine invention of first-order predicates by inverting resolution. In Proc. 5th International Conference on Machine Learning, 339–352, 1988.
E. Shapiro. Inductive inference of theories from facts. Research report 192, Yale Univ., 1981.
T. Shinohara. Rich classes inferable from positive data: length-bounded elementary formal system. Information and computation, 108:175–186, 1994.
A. Yamamoto. Procedural semantics and negative information of elementary formal system. Journal of Logic Programming, 13:89–97, 1992.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sugimoto, N., Hirata, K., Ishizaka, H. (1996). Constructive learning of translations based on dictionaries. In: Arikawa, S., Sharma, A.K. (eds) Algorithmic Learning Theory. ALT 1996. Lecture Notes in Computer Science, vol 1160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61863-5_45
Download citation
DOI: https://doi.org/10.1007/3-540-61863-5_45
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61863-8
Online ISBN: 978-3-540-70719-6
eBook Packages: Springer Book Archive