Dublin city university at clef 2007: cross-language speech retrieval experiments

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Dublin city university at clef 2007: cross-language speech retrieval experiments
Authors: Ying Zhang, Gareth J. F. Jones, Ke Zhang [edit item]
Citation: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007  : . 2008.
Publication type: Conference paper
Peer-reviewed: Yes
Database(s):
DOI: 10.1007/978-3-540-85760-0_89.
Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Dublin city university at clef 2007: cross-language speech retrieval experiments is a publication by Ying Zhang, Gareth J. F. Jones, Ke Zhang.


[edit] Abstract

The Dublin City University participation in the {CLEF} 2007 {CL-SR} English task concentrated primarily on issues of topic translation. Our retrieval system used the {BM25F} model and pseudo relevance feedback. Topics were translated into English using the Yahoo! {BabelFish} free online service combined with domain-specific translation lexicons gathered automatically from Wikipedia. We explored alternative topic translation methods using these resources. Our results indicate that extending machine translation tools using automatically generated domain-specific translation lexicons can provide improved {CLIR} effectiveness for this task.

[edit] Research questions

"The Dublin City University participated in the CLEF 2007 CL-SR English task. For CLEF 2007 we concentrated primarily on the issues of topic translation, combining this with search ¯eld combination and pseudo relevance feedback methods used for our CLEF 2006 submissions. Topics were translated into English using the Yahoo! BabelFish free online translation service combined with domain-speci¯c translation lexicons gathered automatically from Wikipedia. We explored alternative translations methods with document retrieval based the combination of the multiple document ¯elds using the BM25F ¯eld combination model."

Research details

Topics: Other information retrieval topics [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: Experiment [edit item]
Data source: Experiment responses, Wikipedia pages [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Language [edit item]
Wikipedia data extraction: Live Wikipedia [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: English [edit item]

[edit] Conclusion

"This paper has described results for our participation in the CLEF 2007 CL-SR track. In 2007 our experiments focussed on the combination of standard machine translation with domain-speci¯c translation resources. Our results indicate that combining domain-speci¯c translation derived from Wikipedia with the output of standard machine translation can produce substantial im- provements in MAP. Further improvements can also be observed when combined with PRF. However, these trends are not observed consistently in all cases, and further investigations will focus on understanding di®erences in behaviour more clearly and re¯ning our procedures for training domain-speci¯c translation resources."

[edit] Comments


Further notes[edit]

Compiled in Advances in Multilingual and Multimodal Information Retrieval

Facts about "Dublin city university at clef 2007: cross-language speech retrieval experiments"RDF feed
AbstractThe Dublin City University participation iThe Dublin City University participation in the {CLEF} 2007 {CL-SR} English task concentrated primarily on issues of topic translation. Our retrieval system used the {BM25F} model and pseudo relevance feedback. Topics were translated into English using the Yahoo! {BabelFish} free online service combined with domain-specific translation lexicons gathered automatically from Wikipedia. We explored alternative topic translation methods using these resources. Our results indicate that extending machine translation tools using automatically generated domain-specific translation lexicons can provide improved {CLIR} effectiveness for this task.proved {CLIR} effectiveness for this task.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
ConclusionThis paper has described results for our pThis paper has described results for our participation in the CLEF 2007 CL-SR track. In 2007 our

experiments focussed on the combination of standard machine translation with domain-speci¯c translation resources. Our results indicate that combining domain-speci¯c translation derived from Wikipedia with the output of standard machine translation can produce substantial im- provements in MAP. Further improvements can also be observed when combined with PRF. However, these trends are not observed consistently in all cases, and further investigations will focus on understanding di®erences in behaviour more clearly and re¯ning our procedures for training domain-speci¯c translation resources.ning

domain-speci¯c translation resources.
Data sourceExperiment responses + and Wikipedia pages +
Doi10.1007/978-3-540-85760-0 89 +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Dublin%2Bcity%2Buniversity%2Bat%2Bclef%2B2007%3A%2Bcross-language%2Bspeech%2Bretrieval%2Bexperiments%22 +
Has authorYing Zhang +, Gareth J. F. Jones + and Ke Zhang +
Has domainComputer science +
Has topicOther information retrieval topics +
Peer reviewedYes +
Publication typeConference paper +
Published in8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007 +
Research designExperiment +
Research questionsThe Dublin City University participated inThe Dublin City University participated in the CLEF 2007 CL-SR English task. For

CLEF 2007 we concentrated primarily on the issues of topic translation, combining this with search ¯eld combination and pseudo relevance feedback methods used for our CLEF 2006 submissions. Topics were translated into English using the Yahoo! BabelFish free online translation service combined with domain-speci¯c translation lexicons gathered automatically from Wikipedia. We explored alternative translations methods with document retrieval based the combination of the multiple document

¯elds using the BM25F ¯eld combination model.
ds using the BM25F ¯eld combination model.
Revid10,738 +
TheoriesUndetermined
Theory typeDesign and action +
TitleDublin city university at clef 2007: cross-language speech retrieval experiments
Unit of analysisLanguage +
Urlhttp://www.springerlink.com/content/l164524870311084/ +
Wikipedia coverageSample data +
Wikipedia data extractionLive Wikipedia +
Wikipedia languageEnglish +
Wikipedia page typeArticle +
Year2008 +