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Prediction of performance of cross-language information retrieval using automatic evaluation of translation

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Title: Prediction of performance of cross-language information retrieval using automatic evaluation of translation
Authors: Kishida, Kazuaki1 kishida@slis.keio.ac.jp
Source: Library & Information Science Research (07408188). Jun2008, Vol. 30 Issue 2, p138-144. 7p.
Subject Terms: *Information-seeking strategies, *Access to information, Cross-language information retrieval, Precision (Information retrieval), Information retrieval research, Regression analysis, Machine translating, Multilingual thesauri
Abstract: This study develops regression models for predicting the performance of cross-language information retrieval (CLIR). The model assumes that CLIR performance can be explained by two factors: (1) the ease of search inherent in each query and (2) the translation quality in the process of CLIR systems. As operational variables, monolingual information retrieval (IR) performance is used for measuring the ease of search, and the well-known evaluation metric BLEU is used to measure the translation quality. This study also proposes an alternative metric, weighted average for matched unigrams (WAMU), which is tailored to gauging translation quality for special IR purposes. The data for regression analysis are obtained from a retrieval experiment of English-to-Italian bilingual searches using the CLEF 2003 test collection. The CLIR and monolingual IR performances are measured by average precision score. The result shows that the proposed regression model can explain about 60% of the variation in CLIR performance, and WAMU has more predictive power than BLEU. A back translation method for applying the regression model to operational CLIR systems in real situations is discussed. [Copyright &y& Elsevier]
Copyright of Library & Information Science Research (07408188) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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Header DbId: ehh
DbLabel: Education Research Complete
An: 32666255
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Prediction of performance of cross-language information retrieval using automatic evaluation of translation
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  Data: <searchLink fieldCode="AR" term="%22Kishida%2C+Kazuaki%22">Kishida, Kazuaki</searchLink><relatesTo>1</relatesTo><i> kishida@slis.keio.ac.jp</i>
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  Data: <searchLink fieldCode="JN" term="%22Library+%26+Information+Science+Research+%2807408188%29%22">Library & Information Science Research (07408188)</searchLink>. Jun2008, Vol. 30 Issue 2, p138-144. 7p.
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  Data: *<searchLink fieldCode="DE" term="%22Information-seeking+strategies%22">Information-seeking strategies</searchLink><br />*<searchLink fieldCode="DE" term="%22Access+to+information%22">Access to information</searchLink><br /><searchLink fieldCode="DE" term="%22Cross-language+information+retrieval%22">Cross-language information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Precision+%28Information+retrieval%29%22">Precision (Information retrieval)</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval+research%22">Information retrieval research</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+translating%22">Machine translating</searchLink><br /><searchLink fieldCode="DE" term="%22Multilingual+thesauri%22">Multilingual thesauri</searchLink>
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  Data: This study develops regression models for predicting the performance of cross-language information retrieval (CLIR). The model assumes that CLIR performance can be explained by two factors: (1) the ease of search inherent in each query and (2) the translation quality in the process of CLIR systems. As operational variables, monolingual information retrieval (IR) performance is used for measuring the ease of search, and the well-known evaluation metric BLEU is used to measure the translation quality. This study also proposes an alternative metric, weighted average for matched unigrams (WAMU), which is tailored to gauging translation quality for special IR purposes. The data for regression analysis are obtained from a retrieval experiment of English-to-Italian bilingual searches using the CLEF 2003 test collection. The CLIR and monolingual IR performances are measured by average precision score. The result shows that the proposed regression model can explain about 60% of the variation in CLIR performance, and WAMU has more predictive power than BLEU. A back translation method for applying the regression model to operational CLIR systems in real situations is discussed. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Library & Information Science Research (07408188) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.lisr.2007.09.003
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      – Code: eng
        Text: English
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      – SubjectFull: Information-seeking strategies
        Type: general
      – SubjectFull: Access to information
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      – SubjectFull: Cross-language information retrieval
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      – SubjectFull: Precision (Information retrieval)
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      – SubjectFull: Information retrieval research
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      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Machine translating
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      – SubjectFull: Multilingual thesauri
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      – TitleFull: Prediction of performance of cross-language information retrieval using automatic evaluation of translation
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              Text: Jun2008
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              Y: 2008
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