46 citations · 106 across the 7 of their papers we have counts for
7 papers
Hierarchical Non-Emitting Markov Models
Eric Sven Ristad, Robert G. Thomas
We describe a simple variant of the interpolated Markov model with non-emitting state transitions and prove that it is strictly more powerful than any Markov model. More importantl…
Library of Practical Abstractions, Release 1.2
Eric Sven Ristad, Peter N. Yianilos
The library of practical abstractions (LIBPA) provides efficient implementations of conceptually simple abstractions, in the C programming language. We believe that the best librar…
Maximum Entropy Modeling Toolkit
Eric Sven Ristad
The Maximum Entropy Modeling Toolkit supports parameter estimation and prediction for statistical language models in the maximum entropy framework. The maximum entropy framework pr…
Nonuniform Markov models
Eric Sven Ristad, Robert G. Thomas
A statistical language model assigns probability to strings of arbitrary length. Unfortunately, it is not possible to gather reliable statistics on strings of arbitrary length from…
Learning string edit distance
Eric Sven Ristad, Peter N. Yianilos
In many applications, it is necessary to determine the similarity of two strings. A widely-used notion of string similarity is the edit distance: the minimum number of insertions,…
A Natural Law of Succession
Eric Sven Ristad
Consider the problem of multinomial estimation. You are given an alphabet of k distinct symbols and are told that the i-th symbol occurred exactly n_i times in the past. On the bas…
New Techniques for Context Modeling
Eric Sven Ristad, Robert G. Thomas
We introduce three new techniques for statistical language models: extension modeling, nonmonotonic contexts, and the divergence heuristic. Together these techniques result in lang…