Learning weights for translation candidates in Japanese-Chinese information retrieval

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Learning weights for translation candidates in Japanese-Chinese information retrieval
Authors: Chu-Cheng Lin, Yu-Chun Wang, Chih-Hao Yeh, Wei-Chi Tsai, Richard Tzong-Han Tsai [edit item]
Citation: Expert Systems with Applications 36 (4): 7695-7699. 2009.
Publication type: Journal article
Peer-reviewed: Yes
Database(s):
DOI: 10.1016/j.eswa.2008.09.004.
Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Learning weights for translation candidates in Japanese-Chinese information retrieval is a publication by Chu-Cheng Lin, Yu-Chun Wang, Chih-Hao Yeh, Wei-Chi Tsai, Richard Tzong-Han Tsai.


[edit] Abstract

This paper describes our Japanese-Chinese information retrieval system. Our system takes the query-translation" approach. Our system employs both a more conventional bilingual Japanese-Chinese dictionary and Wikipedia for translating query terms. We propose that Wikipedia can be used as a good NE bilingual dictionary. By exploiting the nature of Japanese writing system we propose that query terms be processed differently based on the forms they are written in. We use an iterative method for weight-tuning and term disambiguation which is based on the PageRank algorithm. When evaluating on the NTCIR-5 test set our system achieves as high as 0.2217 and 0.2276 in relax MAP (mean average precision) measurement of T-runs and D-runs.

[edit] Research questions

"his paper describes our Japanese–Chinese information retrieval system. Our system takes the “query-translation” approach. Our system employs both a more conventional bilingual Japanese–Chinese dictionary and Wikipedia for translating query terms. We propose that Wikipedia can be used as a good NE bilingual dictionary. By exploiting the nature of Japanese writing system, we propose that query terms be processed differently based on the forms they are written in. We use an iterative method for weight-tuning and term disambiguation, which is based on the PageRank algorithm."

Research details

Topics: Cross-language information retrieval [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Sample data [edit item]
Theories: "Undetermined" [edit item]
Research design: Mathematical modeling, Statistical analysis [edit item]
Data source: Documents, Wikipedia pages [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Live Wikipedia [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: Chinese, Japanese [edit item]

[edit] Conclusion

"We exploited the nature of Japanese vocabulary and the Japanese writing system for better translations. Using Kanji for translation yields significant improvements in our evaluation. The results of the evaluation confirm that foreign terms are widely transcribed in Katakana.

To cope with ambiguity, we have adopted an iterative disambiguating scheme. The current implementation of this scheme, which uses the likelihood function as its weight function, proved to be effective in the evaluation. Our system has achieved MAP as high as 0.2276, and outperforms the previous NTCIR-5 CLIR Japanese–Chinese T-run’s best rigid MAP by 111%, and D-run’s by 19%."

[edit] Comments


Further notes[edit]

Facts about "Learning weights for translation candidates in Japanese-Chinese information retrieval"RDF feed
AbstractThis paper describes our Japanese-Chinese This paper describes our Japanese-Chinese information retrieval system. Our system takes the query-translation" approach. Our system employs both a more conventional bilingual Japanese-Chinese dictionary and Wikipedia for translating query terms. We propose that Wikipedia can be used as a good NE bilingual dictionary. By exploiting the nature of Japanese writing system we propose that query terms be processed differently based on the forms they are written in. We use an iterative method for weight-tuning and term disambiguation which is based on the PageRank algorithm. When evaluating on the NTCIR-5 test set our system achieves as high as 0.2217 and 0.2276 in relax MAP (mean average precision) measurement of T-runs and D-runs.ecision) measurement of T-runs and D-runs.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
ConclusionWe exploited the nature of Japanese vocabuWe exploited the nature of Japanese vocabulary and the Japanese writing system for better translations. Using Kanji for translation yields significant improvements in our evaluation. The results of the evaluation confirm that foreign terms are widely transcribed in Katakana. To cope with ambiguity, we have adopted an iterative disambiguating scheme. The current implementation of this scheme, which uses the likelihood function as its weight function, proved to be effective in the evaluation. Our system has achieved MAP as high as 0.2276, and outperforms the previous NTCIR-5 CLIR Japanese–Chinese T-run’s best rigid MAP by 111%, and D-run’s by 19%.est rigid MAP by 111%, and D-run’s by 19%.
Data sourceDocuments + and Wikipedia pages +
Doi10.1016/j.eswa.2008.09.004 +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Learning%2Bweights%2Bfor%2Btranslation%2Bcandidates%2Bin%2BJapanese-Chinese%2Binformation%2Bretrieval%22 +
Has authorChu-Cheng Lin +, Yu-Chun Wang +, Chih-Hao Yeh +, Wei-Chi Tsai + and Richard Tzong-Han Tsai +
Has domainComputer science +
Has topicCross-language information retrieval +
Issue4 +
Pages7695-7699 +
Peer reviewedYes +
Publication typeJournal article +
Published inExpert Systems with Applications +
Research designMathematical modeling + and Statistical analysis +
Research questionshis paper describes our Japanese–Chinese ihis paper describes our Japanese–Chinese information retrieval system. Our system takes the “query-translation” approach. Our system employs both a more conventional bilingual Japanese–Chinese dictionary and Wikipedia for translating query terms. We propose that Wikipedia can be used as a good NE bilingual dictionary. By exploiting the nature of Japanese writing system, we propose that query terms be processed differently based on the forms they are written in. We use an iterative method for weight-tuning and term disambiguation, which is based on the PageRank algorithm. which is based on the PageRank algorithm.
Revid10,852 +
TheoriesUndetermined
Theory typeDesign and action +
TitleLearning weights for translation candidates in Japanese-Chinese information retrieval
Unit of analysisArticle +
Urlhttp://dx.doi.org/10.1016/j.eswa.2008.09.004 +
Volume36 +
Wikipedia coverageSample data +
Wikipedia data extractionLive Wikipedia +
Wikipedia languageChinese + and Japanese +
Wikipedia page typeArticle +
Year2009 +