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Saturday, July 4, 2009
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Project |
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Significant progress has been made in natural language processing (NLP) technologies in recent years, but most still do not match human performance. Since many applications of these technologies require human-quality results, some form of manual intervention is necessary.
The success of such applications therefore depends heavily on the extent to which errors can be automatically detected and signaled to a human user. In our project we will attempt to devise a generic method for NLP error detection by studying the problem of Confidence Estimation (CE) in NLP results within a Machine Learning (ML) framework.
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Resources |
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Publications
- Final Report (ps) (pdf)
- Nicola Ueffing and Hermann Ney. (2005) Application of
Word-Level Confidence Measures in Interactive Statistical
Machine Translation. In Proceedings of EAMT 2005,
Budapest, May 2005, pp. 262-270.
- John Blatz, Erin Fitzgerald, George Foster,
Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchis,
and Nicola Ueffing. (2004) Confidence Estimation for Machine
Translation. In Proceedings of Coling 2004, Geneva,
August 2004, pp. 315-321.
- Alex Kulesza and Stuart M. Shieber. (2004) A
Learning Approach to Improving Sentence-Level MT
Evaluation. In TMI 04, Baltimore, October 2004, pp. 75-84.
- Alberto Sanchis. (2004) Estimacion y aplicacion
de medidas de confianza en reconocimiento automatico del
habla. PhD Thesis, Universidad Politecnica de Valencia,
May 2004.
- Nicola Ueffing and Hermann Ney. (2004) Bayes
Decision Rules and Confidence Measures for Statistical Machine
Translation. In EsTAL - España for Natural Language
Processing, Springer Verlag, LNCS, Alicante, Spain, pp. 70-81.
Other Resources
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Team |
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Photos |
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