The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Dec. 10, 2013
Filed:
Jun. 26, 2009
Yun Jin, Daejeon, KR;
OH Woog Kwon, Daejeon, KR;
Ying Shun Wu, Daejeon, KR;
Changhao Yin, Daejeon, KR;
Sung Kwon Choi, Daejeon, KR;
Chang Hyun Kim, Daejeon, KR;
Seong IL Yang, Daejeon, KR;
Ki Young Lee, Daejeon, KR;
Yoon Hyung Roh, Daejeon, KR;
Young AO Seo, Daejeon, KR;
Eun Jin Park, Daejeon, KR;
Young Kii Kim, Daejeon, KR;
Sang Kyu Park, Daejeon, KR;
Yun Jin, Daejeon, KR;
Oh Woog Kwon, Daejeon, KR;
Ying Shun Wu, Daejeon, KR;
Changhao Yin, Daejeon, KR;
Sung Kwon Choi, Daejeon, KR;
Chang Hyun Kim, Daejeon, KR;
Seong Il Yang, Daejeon, KR;
Ki Young Lee, Daejeon, KR;
Yoon Hyung Roh, Daejeon, KR;
Young Ao Seo, Daejeon, KR;
Eun Jin Park, Daejeon, KR;
Young Kii Kim, Daejeon, KR;
Sang Kyu Park, Daejeon, KR;
Electronics and Telecommunications Research Institute, Daejeon, KR;
Abstract
A method for automatically detecting errors in machine translation using a parallel corpus includes analyzing morphemes of a target language sentence in the parallel corpus and a machine-translated target language sentence, corresponding to a source language sentence, to classify the morphemes into words; aligning by words and decoding, respectively, a group of the source language sentence and the machine-translated target language sentence, and a group of the source language sentence and the target language sentence in the parallel corpus; classifying by types errors in the machine-translated target language sentence by making a comparison, word by word, between the decoded target language sentence in the parallel corpus and the decoded machine-translated target language sentence; and computing error information in the machine-translated target language sentence by examining a frequency of occurrence of the classified error types.