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On String Prioritization in Web-Based User Interface Localization

  • Luis A. Leiva
  • Vicent Alabau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8787)

Abstract

We have noticed that most of the current challenges affecting user interface localization could be easily approached if string prioritization would be made possible. In this paper, we tackle these challenges through Nimrod, a web-based internationalization tool that prioritizes user interface strings using a number of discriminative features. As a practical application, we investigate different prioritization strategies for different string categories from Wordpress, a popular open-source content management system with a large message catalog. Further, we contribute with WPLoc, a carefully annotated dataset so that others can reproduce our experiments and build upon this work. Strings in the WPLoc dataset are labeled as relevant and non-relevant, where relevant strings are in turn categorized as critical, informative, or navigational. Using state-of-the-art classifiers, we are able to retrieve strings in these categories with competitive accuracy. Nimrod and the WPLoc dataset are both publicly available for download.

Keywords

Localization L10n Internationalization i18n Translation 

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References

  1. 1.
    Breiman, L.: Bagging predictors. Machine Learning 24(2) (1996)Google Scholar
  2. 2.
    Breiman, L.: Random forests. Machine Learning 45(1) (2001)Google Scholar
  3. 3.
    Cascia, M.L., Sethi, S., Sclaro, S.: Combining textual and visual cues for content- based image retrieval on the world wide web. In: IEEEWorkshop on Content-Based Access of Image and Video Libraries, CBAIVL (1998)Google Scholar
  4. 4.
    le Cessie, S., van Houwelingen, J.: Ridge estimators in logistic regression. Applied Statistics 41(1) (1992)Google Scholar
  5. 5.
    Cleary, J.G., Trigg, L.E.: K*: An instance-based learner using an entropic distance measure. In: 12th International Conference on Machine Learning (1995)Google Scholar
  6. 6.
    Collins, R.W.: Software localization for internet software: Issues and methods. IEEE Software 19(2) (2002)Google Scholar
  7. 7.
    DePalma, D.A., Hegde, V., Pielmeier, H., Stewart, R.G.: The language services market. An annual review of the translation, localization, and interpreting services industry (2013), http://commonsenseadvisory.com
  8. 8.
    Dunne, K.J. (ed.): Perspectives on Localization. John Benjamins Publishing Company (2006)Google Scholar
  9. 9.
    Esselink, B.: A Practical Guide to Localization. John Benjamins Publishing Company (2000)Google Scholar
  10. 10.
    Gettext: The GNU gettext manual. version 0.18.2. (1995), http://www.gnu.org/
  11. 11.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations 11(1) (2009)Google Scholar
  12. 12.
    Hogan, J.M., Ho-Stuart, C., Pham, B.: Key challenges in software internationalisation. In: Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalisation (ACSW Frontiers) (2004)Google Scholar
  13. 13.
    Keniston, K.: Software localization: Notes on technology and culture. Working Paper #26, Massachusetts Institute of Technology (1997)Google Scholar
  14. 14.
    Leiva, L.A., Alabau, V.: An automatically generated interlanguage tailored to speakers of minority but culturally in uenced languages. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI) (2012)Google Scholar
  15. 15.
    Leiva, L.A., Alabau, V.: The impact of visual contextualization on UI localization. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI) (2014)Google Scholar
  16. 16.
    Reinecke, K., Bernstein, A.: Improving performance, perceived usability, and aesthetics with culturally adaptive user interfaces. ACM Transactions on Computer-Human Interaction (TOCHI) 18(2), 8:1–8:29 (2011)Google Scholar
  17. 17.
    Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65(6) (1958)Google Scholar
  18. 18.
    Sun, H.: Building a culturally-competent corporate web site: an exploratory study of cultural markers in multilingual web design. In: Proceedings of the 19th Annual International Conference on Computer Documentation (SIGDOC) (2001)Google Scholar
  19. 19.
    De Troyer, O., Casteleyn, S.: Designing localized web sites. In: Zhou, X., Su, S., Papazoglou, M.P., Orlowska, M.E., Jeffery, K. (eds.) WISE 2004. LNCS, vol. 3306, pp. 547–558. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    VanReusel, J.F.: Five golden rules to achieve agile localization (2013), http://blogs.adobe.com/globalization/
  21. 21.
    Wang, X., Zhang, L., Xie, T., Mei, H., Sun, J.: TranStrL: An automatic need-to- translate string locator for software internationlization. In: Proceedings of IEEE 31st International Conference on Software Engineering (ICSE) (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Luis A. Leiva
    • 1
  • Vicent Alabau
    • 1
  1. 1.PRHLT Research CenterUniversitat Politècnica de ValènciaSpain

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