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Adapting word prediction to subject matter without topic-labeled data

Published: 13 October 2008 Publication History

Abstract

Word prediction helps to increase communication rate when using Augmentative and Alternative Communication devices. Basic prediction systems offer topically inappropriate predictions for the context, thus we adapt the predictions to the topic of discourse. However, previous work has relied on texts that are grouped into topics by humans. In contrast, we avoid this restriction by treating each document as a topic. The results are comparable to human-labeled topics and also the method is applicable to unlabeled text.

References

[1]
J. Li and G. Hirst. Semantic knowledge in word completion. In ASSETS, pages 121--128, 2005.
[2]
M. Mahajan, D. Beeferman, and X. D. Huang. Improved topic-dependent language modeling using information retrieval techniques. In IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 541--544, 1999.
[3]
J. Matiasek and M. Baroni. Exploiting long distance collocational relations in predictive typing. In EACL-03 Workshop on Language Modeling for Text Entry, pages 1--8, 2003.
[4]
K. Trnka and K. F. McCoy. Corpus Studies in Word Prediction. In ASSETS, pages 195--202, 2007.
[5]
T. Wandmacher and J. Antoine. Methods to integrate a language model with semantic information for a word prediction component. In EMNLP-CoNLL, pages 506--513, 2007.

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cover image ACM Conferences
Assets '08: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
October 2008
332 pages
ISBN:9781595939760
DOI:10.1145/1414471
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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2008

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Author Tags

  1. language modeling
  2. statistical methods
  3. topic modeling
  4. word prediction

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ASSETS08
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