Oard, D., Gonzalo, J., Sanderson, M. et al. (2 more authors) (2004) Interactive cross-language document selection. Information Retrieval, 7 (1-2). pp. 205-228. ISSN 1573-7659
Abstract
The problem of finding documents written in a language that the searcher cannot read is perhaps the most challenging application of cross-language information retrieval technology. In interactive applications, that task involves at least two steps: (1) the machine locates promising documents in a collection that is larger than the searcher could scan, and (2) the searcher recognizes documents relevant to their intended use from among those nominated by the machine. This article presents the results of experiments designed to explore three techniques for supporting interactive relevance assessment: (1) full machine translation, (2) rapid term-by-term translation, and (3) focused phrase translation. Machine translation was found to better support this task than term-by-term translation, and focused phrase translation further improved recall without an adverse effect on precision. The article concludes with an assessment of the strengths and weaknesses of the evaluation framework used in this study and some remarks on implications of these results for future evaluation campaigns.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2004 Kluwer Academic Publishers. This is an author produced version of the published paper. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Repository Officer |
Date Deposited: | 23 Sep 2008 12:24 |
Last Modified: | 08 Jun 2014 19:04 |
Published Version: | http://dx.doi.org/10.1023/B:INRT.0000009446.22036.... |
Status: | Published |
Publisher: | Springer |
Refereed: | Yes |
Identification Number: | 10.1023/B:INRT.0000009446.22036.e3 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:4560 |