The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
An empirical study of smoothing techniques for language modeling
Open accessAbstract
We present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by Jelinek and Mercer (1980), Katz (1987), and Church and Gale (1991). We investigate for the first time how factors such as training data size, corpus (e.g., Brown versus Wall Street Journal), and n-gram order (bigram versus trigram) affect the relative performance of these methods, which we measure through the cross-entropy of test data. In addition, we introduce two novel smoothing techniques, one a variation of Jelinek-Mercer smoothing and one a very simple linear interpolation technique, both of which outperform existing methods.
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Cites
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
(1992) · doi:10.5555/176313.176316 · Cited by 2,892
SWITCHBOARD: telephone speech corpus for research and development
Godfrey, J. J., Holliman, E. C., & McDaniel, J. (1992) · [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1992.225858 · Cited by 2,134
Cited by
“Librispeech: an ASR corpus based on public domain audio books,”
Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178964 · Cited by 6,072
Character-Aware Neural Language Models
Kim, Y., Jernite, Y., Sontag, D., & Rush, A. M. (2015) · arXiv · doi:10.48550/arxiv.1508.06615 · Cited by 1,530
One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling
Chelba, C., Mikolov, T., Schuster, M., Ge, Q., Brants, T., Koehn, P., & Robinson, T. (2013) · arXiv · doi:10.48550/arxiv.1312.3005 · Cited by 1,051
Are deep neural networks the best choice for modeling source code?
Hellendoorn, V. J., & Devanbu, P. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106290 · Cited by 292
Who Are You? A Statistical Approach to Measuring User Authenticity
Freeman, D., Jain, S., Duermuth, M., Biggio, B., & Giacinto, G. (2016) · Proceedings 2016 Network and Distributed System Security Symposium · doi:10.14722/ndss.2016.23240 · Cited by 123
Metadata
- Authors
- Chen, S. F., & Goodman, J.
- Venue
- Proceedings of the 34th annual meeting on Association for Computational Linguistics -
- Published
- 1996
- Pages
- 310-318
- OpenAlex
- W2097927681
- Cited by
- 770
- References
- 3
- FWCI
- 2.40
- Type
- conference paper
- Language
- en
- Open access
- bronze
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