IEICE TRANSACTIONS on Information
Example-Based Transfer of Japanese Adnominal Particles into English
Summary :
This paper deals with the problem of translating Japanese adnominal particles into English according to the idea of Example-Based Machine Translation (EBMT) proposed by Nagao. Japanese adnominal particles are important because: (1) they are frequent function words; (2) to translate them into English is difficult because their translations are diversified; (3) EBMT's effectiveness for adnominal particles suggests that EBMT is effective for other function words, e. g., prepositions of European languages. In EBMT, (1) a database which consists of examples (pairs of a source language expression and its target language translation) is prepared as knowledge for translation; (2) an example whose source expression is similar to the input phrase or sentence is retrieved from the example database; (3) by replacements of corresponding words in the target expression of the retrieved example, the translation is obtained. The similarity in EBMT is computed by the summation of the distance between words multiplied by the weight of each word. The authors' method differs from preceding research in two important points: (1) the authors utilize a general thesaurus to compute the distance between words; (2) the authors propose a weight which changes for every input. The feasibility of our approach has been proven through experiments concerning success rate.
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- IEICE TRANSACTIONS on Information Vol.E75-D No.4 pp.585-594
- Publication Date
- 1992/07/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence and Cognitive Science
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Eiichiro SUMITA, Hitoshi IIDA, "Example-Based Transfer of Japanese Adnominal Particles into English" in IEICE TRANSACTIONS on Information,
vol. E75-D, no. 4, pp. 585-594, July 1992, doi: .
Abstract: This paper deals with the problem of translating Japanese adnominal particles into English according to the idea of Example-Based Machine Translation (EBMT) proposed by Nagao. Japanese adnominal particles are important because: (1) they are frequent function words; (2) to translate them into English is difficult because their translations are diversified; (3) EBMT's effectiveness for adnominal particles suggests that EBMT is effective for other function words, e. g., prepositions of European languages. In EBMT, (1) a database which consists of examples (pairs of a source language expression and its target language translation) is prepared as knowledge for translation; (2) an example whose source expression is similar to the input phrase or sentence is retrieved from the example database; (3) by replacements of corresponding words in the target expression of the retrieved example, the translation is obtained. The similarity in EBMT is computed by the summation of the distance between words multiplied by the weight of each word. The authors' method differs from preceding research in two important points: (1) the authors utilize a general thesaurus to compute the distance between words; (2) the authors propose a weight which changes for every input. The feasibility of our approach has been proven through experiments concerning success rate.
URL: https://global.ieice.org/en_transactions/information/10.1587/e75-d_4_585/_p
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@ARTICLE{e75-d_4_585,
author={Eiichiro SUMITA, Hitoshi IIDA, },
journal={IEICE TRANSACTIONS on Information},
title={Example-Based Transfer of Japanese Adnominal Particles into English},
year={1992},
volume={E75-D},
number={4},
pages={585-594},
abstract={This paper deals with the problem of translating Japanese adnominal particles into English according to the idea of Example-Based Machine Translation (EBMT) proposed by Nagao. Japanese adnominal particles are important because: (1) they are frequent function words; (2) to translate them into English is difficult because their translations are diversified; (3) EBMT's effectiveness for adnominal particles suggests that EBMT is effective for other function words, e. g., prepositions of European languages. In EBMT, (1) a database which consists of examples (pairs of a source language expression and its target language translation) is prepared as knowledge for translation; (2) an example whose source expression is similar to the input phrase or sentence is retrieved from the example database; (3) by replacements of corresponding words in the target expression of the retrieved example, the translation is obtained. The similarity in EBMT is computed by the summation of the distance between words multiplied by the weight of each word. The authors' method differs from preceding research in two important points: (1) the authors utilize a general thesaurus to compute the distance between words; (2) the authors propose a weight which changes for every input. The feasibility of our approach has been proven through experiments concerning success rate.},
keywords={},
doi={},
ISSN={},
month={July},}
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TY - JOUR
TI - Example-Based Transfer of Japanese Adnominal Particles into English
T2 - IEICE TRANSACTIONS on Information
SP - 585
EP - 594
AU - Eiichiro SUMITA
AU - Hitoshi IIDA
PY - 1992
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E75-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - July 1992
AB - This paper deals with the problem of translating Japanese adnominal particles into English according to the idea of Example-Based Machine Translation (EBMT) proposed by Nagao. Japanese adnominal particles are important because: (1) they are frequent function words; (2) to translate them into English is difficult because their translations are diversified; (3) EBMT's effectiveness for adnominal particles suggests that EBMT is effective for other function words, e. g., prepositions of European languages. In EBMT, (1) a database which consists of examples (pairs of a source language expression and its target language translation) is prepared as knowledge for translation; (2) an example whose source expression is similar to the input phrase or sentence is retrieved from the example database; (3) by replacements of corresponding words in the target expression of the retrieved example, the translation is obtained. The similarity in EBMT is computed by the summation of the distance between words multiplied by the weight of each word. The authors' method differs from preceding research in two important points: (1) the authors utilize a general thesaurus to compute the distance between words; (2) the authors propose a weight which changes for every input. The feasibility of our approach has been proven through experiments concerning success rate.
ER -
English
