Skip to main content

Statistical-Based Abbreviation Expansion

  • Conference paper
Text, Speech and Dialogue (TSD 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6836))

Included in the following conference series:

  • 922 Accesses

Abstract

The work presented in this paper deals with the text normalization for highly inflectional languages. This paper is focused on abbreviation expansion and likewise on numerals normalization. Our text normalization system does not use any explicit parser or part-of-speech tagger and thus it can be called lightly supervised. The standard rule-based text normalization method is compared with the proposed statistical-based one in the task of expansion of Czech abbreviations.

This research was supported by the Grant Agency of the Czech Republic, project No. GAČR 102/08/0707 and the Technology Agency of the Czech Republic, project No. TA01011264 and the Ministry of Education of the Czech Republic, project No. MŠMT LC536.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Hippman, R., Dostálová, T., Zvárová, J., Nagy, M., Seydlová, M., Hanzlíček, P., Kříž, P., Šmídl, L., Trmal, J.: Voice-supported electronic health record for temporomandibular joint disorders. Methods of Information in Medicine 49, 168–172 (2010)

    Article  Google Scholar 

  2. Caruana, R., Niculescu-Mizil, A.: Data mining in metric space: An empirical analysis of supervised learning performance criteria, pp. 69–78. ACM Press, New York (2004)

    Google Scholar 

  3. Shen, Y.: Loss Functions for Binary Classification and Class Probability Estimation. PhD thesis (2005)

    Google Scholar 

  4. Sproat, R.: Lightly supervised learning of text normalization: Russian number names. In: IEEE Workshop on Spoken Language Technology, Berkeley, U.S.A (2010)

    Google Scholar 

  5. Schlippe, T., Zhu, C., Gebhardt, J., Schultz, T.: Text normalization based on statistical machine translation and internet user support. In: INTERSPEECH, pp. 1816–1819 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zelinka, J., Romportl, J., Müller, L. (2011). Statistical-Based Abbreviation Expansion. In: Habernal, I., Matoušek, V. (eds) Text, Speech and Dialogue. TSD 2011. Lecture Notes in Computer Science(), vol 6836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23538-2_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23538-2_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23537-5

  • Online ISBN: 978-3-642-23538-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics