Abstract
On our proposed method, source language is translated into target language via Number Representation. A text in the source language is translated into a number representation text. The number representation text is the number string corresponding to the original source language text. The number representation text is translated into a number representation text for the target language. The number representation text is translated into a text in the target language. The text is the translation result finally. A number representation text is more abstract than the original text because the number representation text corresponds to several texts. The system based on our proposed method is able to acquire more translation rules on number representation than that on the original text by Inductive Learning. Moreover, the system disambiguates number representation by its own adaptability. In the experiment, the correct translation rate for our proposed method is higher than that for the method without number representation. Thus, it is proved that our proposed method is more effective for machine translation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
S. Sato.: MBT2: a method for combining fragments of examples in example-based translation. In Artificial Intelligence, volume 75, pages 31–49, May 1995.
P. F. Brown et al.: A Statistical Approach to Machine Translation. In Computational Linguistics, volume 16, number 2, pages 79–85, June 1990.
K. Araki, H. Echizen-ya and K. Tochinai.: Performance Evaluation in Travel English for GA-ILMT. In Proceedings of the IASTED International Conference Artificial Intelligence and Soft Computing, pages 117–120, Ban., Canada, July 1997.
H. Echizen-ya, K. Araki, Y. Momouchi and K. Tochinai.: A Study of Performance Evaluation for GA-ILMT Using Travel English. In Proceedings of the 13th Pacific Asia Conference on Language, Information and Computation, pages 285–292, Taipei, Taiwan, February 1999.
M. Matsuhara, K. Araki, Y. Momouchi and K. Tochinai.: Evaluation of Number-Kanji Translation Method of Non-Segmented Japanese Sentences Using Inductive Learning with Degenerated Input. In N. Foo (Ed.): Advanced Topics in Artificial Intelligence(AI’99), Lecture Note in Artificial Intelligence 1747, pages 474–475, December 1999.
M. Matsuhara, K. Araki, Y. Momouchi and K. Tochinai.: Evaluation of Number-Kanji Translation Method Using Inductive Learning on E-mail. In Proceedings of the IASTED International Conference Artificial Intelligence and Soft Computing, pages 487–493, Ban., Canada, July 2000.
Y. Araki and J. Lee.: Travel English pocket book. Nihon Bungei Sha, (Tokyo), 1995.
Ryokou Kaiwa Kenkyuukai.: Kaigai Ryokou Eikaiwa. Jitugyou no Nihon Sha, (Tokyo), 1980.
K. Gilbert.: Kent no Travel Eikaiwa. Jitugyou no Nihon Sha, (Tokyo), 1995.
Y. Ishikawa and Travel Communication Kenkyuukai.: A Timely Handbook for Single Travelers Travel English. Jitugyou no Nihon Sha, (Tokyo), 1995.
Y. Maekawa.: America wo Jiyuu ni Aruku Tabi no Beikaiwa. Ikeda Shoten, (Tokyo), 1994.
Tikyuu no Arukikata Hensyuusitu.: Tabi no Kaiwasyuu 2 Beigo/Eigo. Diamond Sha, (Tokyo), 1993.
Book Maker.: Kaigai Ryokou Kantan Eikaiwa Hand Book. Ikeda Shoten, (Tokyo), 1996.
Junko Kai.: Hitori Aruki no Eigo Jiyuujizai. Nihonn Koutuu Kousha Shuppan Jigyou Kyouku, (Tokyo), 1991.
W. Read.: Komatta Toki no Travel Eikaiwa Nyuumon. Nihon Bungei Sha, (Tokyo), 1995.
A. Saito.: Rokkakokugo Kaiwa I pocket interpreter. Nihon Koutuu Kousha Shuppan Jigyou Kyoku, (Tokyo), 1960.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Matsuhara, M., Araki, K., Tochinai, K. (2002). Effectiveness for Machine Translation Method Using Inductive Learning on Number Representation. In: McKay, B., Slaney, J. (eds) AI 2002: Advances in Artificial Intelligence. AI 2002. Lecture Notes in Computer Science(), vol 2557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36187-1_57
Download citation
DOI: https://doi.org/10.1007/3-540-36187-1_57
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00197-3
Online ISBN: 978-3-540-36187-9
eBook Packages: Springer Book Archive