Skip to main content

Natural Intelligence in a Machine Translation System

  • Conference paper
  • First Online:
Machine Translation: From Research to Real Users (AMTA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2499))

Included in the following conference series:

Abstract

Any-Language Communications has developed a novel semantics-oriented pre-market prototype system, based on the Theory of Universal Grammar, that uses the innate relationships of the words in a sensible sentence (the natural intelligence) to determine the true contextual meaning of all the words. The system is built on a class/category structure of language concepts and includes a weighted inheritance system, a number language word conversion, and a tailored genetic algorithm to select the best of the possible word meanings. By incorporating all of the language information within the dictionaries, the same semantic processing code is used to interpret any language. This approach is suitable for machine translation (MT), sophisticated text mining, and artificial intelligence applications. An MT system has been tested with English, French, German, Hindi, and Russian. Sentences for each of those languages have been successfully interpreted and proper translations generated.

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. Brown, P., Della Pietra, S., Della Pietra, V., Mercer, R.: The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics 19 (1993) 263–311

    Google Scholar 

  2. Richardson, S., Dolan, W., Menezes, A., Pinkham, J.: Achieving Commercial-Quality Translation with Example-Based Methods. In: Proceedings of the VHIth MT Summit, Santiago de Compostela, Spain (2001) 293–298

    Google Scholar 

  3. Technologic. http://www.talaris.com/assets/news_pdf/cp.pdf Computer Letter. March 11 (2002)

  4. Chomsky, N.: Syntactic Structures. Mouton, The Hague (1957)

    Google Scholar 

  5. Hoffman, M. S. (ed.): The World Almanac and Book of Facts, 1990. Pharo Books, New York (1990)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bender, H.J. (2002). Natural Intelligence in a Machine Translation System. In: Richardson, S.D. (eds) Machine Translation: From Research to Real Users. AMTA 2002. Lecture Notes in Computer Science(), vol 2499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45820-4_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-45820-4_24

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44282-0

  • Online ISBN: 978-3-540-45820-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics