An Empirical Study of Smoothing Techniques for Language Modeling.¶
Chen, S. F., & Goodman, J. (1996). An Empirical Study of Smoothing Techniques for Language Modeling. Proceedings of the 34th Annual Meeting of the ACL, 310-318.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Cromwell's Rule
- Machine learning and language modeling — zero-count events destroy probability estimates and yield undefined perplexity; smoothing methods (Laplace, Good–Turing, Kneser–Ney, backoff) exist precisely to keep unseen events at small but nonzero probability.
This sourceSurveys and compares smoothing methods (add-one/Laplace, Good–Turing, Kneser–Ney, backoff) that keep unseen events at small but nonzero probability.
- Machine learning and language modeling — zero-count events destroy probability estimates and yield undefined perplexity; smoothing methods (Laplace, Good–Turing, Kneser–Ney, backoff) exist precisely to keep unseen events at small but nonzero probability.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:74077e16c61d · see in the full table