skip to main content
10.1145/1111449.1111509acmconferencesArticle/Chapter ViewAbstractPublication PagesiuiConference Proceedingsconference-collections
Article

Topic modeling in fringe word prediction for AAC

Published:29 January 2006Publication History

ABSTRACT

Word prediction can be used for enhancing the communication ability of persons with speech and language impairments. In this work, we explore two methods of adapting a language model to the topic of conversation, and apply these methods to the prediction of fringe words.

References

  1. B. Baker. Minspeak. Byte, pages 186--202, 1982.Google ScholarGoogle Scholar
  2. J. Bellegarda. Large vocabulary speech recognition with multispan language models. IEEE Trans. On Speech and Audio Processing, 8(1), 2000.Google ScholarGoogle ScholarCross RefCross Ref
  3. D. R. Beukelman and P. Mirenda. Augmentative and alternative communication: Management of severe communication disorders in children and adults. P.H. Brookes Pub. Co., 1998.Google ScholarGoogle Scholar
  4. L. Boggess. Two simple prediction algorithms to facilitate text production. In Proceedings of the second conference on Applied natural language processing, pages 33--40, Morristown, NJ, USA, 1988. Association for Computational Linguistics. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. A. Carlberger, J. Carlberger, T. Magnuson, M. S. Hunnicutt, S. Palazuelos-Cagigas, and S. A. Navarro. Profet, a new generation of word prediction: An evaluation study. In Proceedings of Natural Language Processing for Communication Aids, 1997.Google ScholarGoogle Scholar
  6. S. Chen, K. Seymore, and R. Rosenfeld. Topic adaptation for language modeling using unnormalized exponential models. In Proc. Int'l Conf. on Acoustics, Speech and Signal Processing, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  7. A. Copestake. Augmented and alternative nlp techniques for augmentative and alternative communication. In Proceedings of the ACL workshop on Natural Language Processing for Communication Aids, 1997.Google ScholarGoogle Scholar
  8. A. Fazly and G. Hirst. Testing the efficacy of part-of-speech information in word completion. In Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  9. R. Florian and D. Yarowsky. Dynamic nonlocal language modeling via hierarchical topic-based adaptation. In Proceedings of ACL'99, pages 167--174, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. N. Garay-Vitoria and J. González-Abascal. Intelligent word-prediction to enhance text input rate. In Proceedings of the second international conference on Intelligent User Interfaces, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. Hindle. Deterministic parsing of syntactic non-fluencies. In Proceedings of the 21st Annual Meeting of the Association for Computational Linguistics, 1983. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. G. Lesher, B. Moulton, and J. Higgonbotham. Effects of ngram order and training text size on word prediction. In Proceedings of the RESNA '99 Annual Conference, 1999.Google ScholarGoogle Scholar
  13. G. Lesher and G. Rinkus. Domain-specific word prediction for augmentative communication. In Proceedings of the RESNA '01 Annual Conference, 2001.Google ScholarGoogle Scholar
  14. M. Mahajan, D. Beeferman, and X. D. Huang. Improved topic-dependent language modeling using information retrieval techniques. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. C. Manning and H. Schütze. Foundations of Statistical Natural Language Processing. MIT Press, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. A. Newell, S. Langer, and M. Hickey. The rôle of natural language processing in alternative and augmentative communication. Natural Language Engineering, 4(1):1--16, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. K. Seymore and R. Rosenfeld. Using story topics for language model adaptation. In Proceedings of Eurospeech '97, pages 1987--1990, Rhodes, Greece, 1997.Google ScholarGoogle Scholar
  18. A. L. Swiffin, J. A. Pickering, J. L. Arnott, and A. F. Newell. Pal: An effort efficient portable communication aid and keyboard emulator. In Proceedings of the 8th Annual Conference on Rehabilitation Techonology, pages 197--199, 1985.Google ScholarGoogle Scholar

Index Terms

  1. Topic modeling in fringe word prediction for AAC

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      IUI '06: Proceedings of the 11th international conference on Intelligent user interfaces
      January 2006
      392 pages
      ISBN:1595932879
      DOI:10.1145/1111449

      Copyright © 2006 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 29 January 2006

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • Article

      Acceptance Rates

      Overall Acceptance Rate746of2,811submissions,27%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader