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Adaptive language modeling using the maximum entropy principle.” Human Language Technology (1993)
| Content Provider | CiteSeerX |
|---|---|
| Author | Lau, Raymond Rosenfel, Ronald Roukos, Salim |
| Description | Proceedings of a Workshop Held at Plainsboro We describe our ongoing efforts at adaptive statistical language modeling. Central to our approach is the Maximum Entropy (ME) Principle, allowing us to combine evidence from multiple sources, such as long-distance triggers and conventional short.distance trigrams. Given consistent statistical evidence, a unique ME solution is guaranteed to exist, and an iterative algorithm exists which is guaranteed to converge to it. Among the advantages of this approach are its simplicity, its generality, and its incremental nature. Among its disadvantages are its computational requirements. We describe a succession of ME models, culminating in our current Maximum Likelihood / Maximum Entropy (ML/ME) model. Preliminary results with the latter show a 27 % perplexity reduction as compared to a conventional trigram model. 1. STATE OF THE ART |
| File Format | |
| Language | English |
| Publisher | Morgan Kaufmann Publishers, Inc |
| Publisher Date | 1993-01-01 |
| Access Restriction | Open |
| Subject Keyword | Adaptive Language Long-distance Trigger Human Language Technology Ongoing Effort Consistent Statistical Evidence Multiple Source Conventional Trigram Model Unique Solution Current Maximum Likelihood Maximum Entropy Adaptive Statistical Language Modeling Incremental Nature Latter Show Maximum Entropy Principle Iterative Algorithm Exists Maximum Entropy Preliminary Result Computational Requirement Perplexity Reduction Distance Trigram |
| Content Type | Text |
| Resource Type | Article |
