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Search between Chinese and Japanese text collections
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In: http://www.mt-archive.info/NTCIR-2007-Gey.pdf (2007)
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Search between Chinese and Japanese text collections
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings6/NTCIR/56.pdf (2007)
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New directions in multilingual information access: Introduction to the workshop at SIGIR 2006
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In: http://www.sigir.org/forum/2006D/2006d_sigirforum_gey.pdf (2006)
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The query answering system Prodicos
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In: Proceedings of Accessing Multilingual Information Repositories: 6th Workshop of the Cross-Language Evaluation Forum, CLEF 2005, Revised Selected Papers, Vienna, Austria, September 2005 ; CLEF 2005 ; https://hal.archives-ouvertes.fr/hal-00444437 ; CLEF 2005, 2006, Austria. pp.527--534 (2006)
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How Similar are Chinese and Japanese for Cross-Language Information Retrieval
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In: http://www.mt-archive.info/NTCIR-2005-Gey.pdf (2005)
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Geotemporal access to multilingual documents
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In: http://ucdata.berkeley.edu/staff/gey/papers/geotemporal-access-ecdl-2004-poster.pdf (2004)
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Berkeley at NTCIR-2: Chinese, Japanese, and English IR Experiments
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In: http://metadata.sims.berkeley.edu/GrantSupported/./papers/ntcir2001.pdf (2001)
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Research to Improve Cross-Language Retrieval { Position Paper for CLEF
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In: http://metadata.sims.berkeley.edu/GrantSupported/./papers/clef-position-paper.ps (2001)
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Abstract:
. Improvement in cross-language information retrieval results can come from a variety of sources { failure analysis, resource enrichment in terms of stemming and parallel and comparable corpora, use of pivot languages, as well as phonetic transliteration and Romanization. Application of these methodologies should contribute to a gradual increase in the ability of search software to cross the language barrier. 1 Failure Analysis In my opinion there has been a dearth of detailed failure analysis in crosslanguage information retrieval, even among the best-performing methods in comparative evaluations at TREC, CLEF, and NTCIR[7]. Just as a post-mortem can determine causation in mortality, a query-by-query analysis can often shed light on why some approaches succeed and others fail. Among the sets of queries utilized in these evaluations we always nd several queries which all participants perform poorly (as measured by the median precision over all runs for that query). When the bes.
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.2084 http://metadata.sims.berkeley.edu/GrantSupported/./papers/clef-position-paper.ps
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Automatic Construction of a Japanese-English Lexicon and its Application in Cross-Language Information Retrieval
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In: http://www.clis.umd.edu/conferences/midas/papers/chen.ps (1999)
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English-German Cross-Language Retrieval for the GIRT Collection - Exploiting a Multilingual Thesaurus
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In: http://trec.nist.gov/pubs/trec8/papers/Berkeley-GIRT-trec8.pdf (1999)
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English-German Cross-Language Retrieval for the GIRT Collection -- Exploiting a Multilingual Thesaurus
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In: http://trec.nist.gov/pubs/trec8/./papers/Berkeley-GIRT-trec8.ps (1999)
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Manual Queries and Machine Translation in Cross-language Retrieval and Interactive Retrieval with Cheshire II at TREC-7
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In: http://trec.nist.gov/pubs/trec7/papers/berkeley.trec7.ps (1998)
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Phrase Discovery for English and Cross-language Retrieval at TREC-6
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In: http://trec.nist.gov/pubs/trec6/papers/brkly.ps (1998)
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Berkeley at NTCIR-2: Chinese, Japanese, and English IR Experiments
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings2/ntcir2_berkeley.pdf
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Prospects for Machine Translation of the Tamil Language
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In: http://www.infitt.org/ti2002/papers/47FGEY.PDF
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Working with Russian Queries for the GIRT, Bilingual and Multilingual CLEF Tasks
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In: http://www.ercim.eu/publication/ws-proceedings/CLEF2/gey.pdf
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Zacharek A, Jiang H
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In: http://metadata.sims.berkeley.edu/papers/clef-main-paper.pdf
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Research to improve Cross-Language Retrieval. Position Paper for CLEF
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In: http://metadata.sims.berkeley.edu/papers/clef-position-paper.pdf
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and
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In: http://www.glue.umd.edu/~oard/papers/trec2002overview.pdf
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and
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In: http://terpconnect.umd.edu/~oard/pdf/trec2002overview.pdf
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