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Comparable Multilingual Patents as Large-Scale Parallel Corpora

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Building and Using Comparable Corpora

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Abstract

Parallel corpora are critical resources for building many NLP applications, ranging from machine translation (MT) to cross-lingual information retrieval. In this chapter, we explore a new but important area involving patents by investigating the potential of cultivating large-scale parallel corpora from comparable multilingual patents. Two major issues are investigated on multilingual patents: (1) How to build large-scale corpora of comparable patents involving many languages? (2) How to mine high-quality parallel sentences from these comparable patents? Four parallel corpora are presented as examples, and some preliminary SMT experiments are reported. We further investigate and show the considerable potential of cultivating large-scale parallel corpora from multilingual patents for a wide variety of languages, such as English, Chinese, Japanese, Korean, German, etc, which would to some extent reduce the parallel data acquisition bottleneck in multilingual information processing.

This chapter is based on the authors’ previous work described in Lu et al. (2009, 2010a, 2010b, 2011)

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Notes

  1. 1.

    http://www.itl.nist.gov/iad/mig/tests/mt/.

  2. 2.

    Anyone interested in the corpus are invited to contact the authors for more details.

  3. 3.

    Retrieved March 2010, from http://www.collinslanguage.com/.

  4. 4.

    Retrieved April, 2010 from http://www.wipo.int/pctdb/en/. The data below involving PCT patents comes from the website of WIPO.

  5. 5.

    http://www.sipo.gov.cn/

  6. 6.

    Some contents are in image format. Thus the images were OCRed and the characters recognized were manually verified.

  7. 7.

    Some contents of the English patents were OCRed by WIPO.

  8. 8.

    http://www.ipdl.inpit.go.jp/homepg.ipdl

  9. 9.

    http://chasen.naist.jp/hiki/ChaSen/

  10. 10.

    http://projects.ldc.upenn.edu/Chinese/LDC_ch.htm

  11. 11.

    Correct means the English sentence is exactly the literal translation of the Chinese one, or the content overlap between them are above 80 % with no need to consider phrasal reordering during the translation; partially correct means the Chinese sentence and the English one are not the literal translation of each other, but the content of each sentence can cover more than 50 % of the other; incorrect means the contents of the Chinese sentence and the English one are not related, or more than 50 % of the content of one sentence is not translated in the other. Please see [17] for more details.

  12. 12.

    http://en.wikipedia.org/wiki/Wikipedia_database

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Acknowledgments

We wish to thank our colleagues, Dr. Kataoka S. and Mr. Wrong B. and others, for their help in evaluating the sampled sentence pairs and triplets.

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Authors and Affiliations

  1. Department of Chinese, Translation and Linguistics, City University of Hong Kong, Kowloon, Hong Kong

    Bin Lu & Benjamin K. Tsou

  2. Research Centre on Linguistics and Language Information Sciences, Hong Kong Institute of Education, New Territories, Hong Kong

    Bin Lu, Ka Po Chow & Benjamin K. Tsou

  3. Hong Kong Institute of Education, New Territories, Hong Kong

    Ka Po Chow

Authors
  1. Bin Lu
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  2. Ka Po Chow
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Correspondence to Bin Lu .

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Editors and Affiliations

  1. Centre for Translation Studies, University of Leeds, Leeds, United Kingdom

    Serge Sharoff

  2. University of Mainz, Mainz, Germany

    Reinhard Rapp

  3. Université de Paris-Sud LIMSI-CNRS, Orsay, France

    Pierre Zweigenbaum

  4. Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, People's Republic of China

    Pascale Fung

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Lu, B., Chow, K.P., Tsou, B.K. (2013). Comparable Multilingual Patents as Large-Scale Parallel Corpora. In: Sharoff, S., Rapp, R., Zweigenbaum, P., Fung, P. (eds) Building and Using Comparable Corpora. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20128-8_9

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  • DOI: https://doi.org/10.1007/978-3-642-20128-8_9

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  • Online ISBN: 978-3-642-20128-8

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