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A Natural Law of Succession
, 1995
"... Consider the following problem. You are given an alphabet of k distinct symbols and are told that the i th symbol occurred exactly ni times in the past. On the basis of this information alone, you must now estimate the conditional probability that the next symbol will be i. In this report, we presen ..."
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Cited by 35 (3 self)
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Consider the following problem. You are given an alphabet of k distinct symbols and are told that the i th symbol occurred exactly ni times in the past. On the basis of this information alone, you must now estimate the conditional probability that the next symbol will be i. In this report, we present a new solution to this fundamental problem in statistics and demonstrate that our solution outperforms standard approaches, both in theory and in practice.
New Techniques for Context Modeling
, 1995
"... We introduce three new techniques for statistical language models: extension modeling, nonmonotonic contexts, and the divergence heuristic. Together these techniques result in language models that have few states, even fewer parameters, and low message entropies. ..."
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Cited by 13 (2 self)
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We introduce three new techniques for statistical language models: extension modeling, nonmonotonic contexts, and the divergence heuristic. Together these techniques result in language models that have few states, even fewer parameters, and low message entropies.
Hierarchical NonEmitting Markov Models
, 1998
"... We describe a simple variant of the interpolated Markov model with nonemitting state transitions and prove that it is strictly more powerful than any Markov model. More importantly, the nonemitting model outperforms the classic interpolated model on natural language texts under a wide range of expe ..."
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Cited by 4 (2 self)
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We describe a simple variant of the interpolated Markov model with nonemitting state transitions and prove that it is strictly more powerful than any Markov model. More importantly, the nonemitting model outperforms the classic interpolated model on natural language texts under a wide range of experimental conditions, with only a modest increase in computational requirements. The nonemitting model is also much less prone to overfitting.