Semi-supervised learning with graphs
- Xiaojin Zhu, J. Lafferty, R. Rosenfeld
- Computer Science
- 2005
A series of novel semi-supervised learning approaches arising from a graph representation, where labeled and unlabeled instances are represented as vertices, and edges encode the similarity between instances are presented.
Adaptive Statistical Language Modeling; A Maximum Entropy Approach
- R. Rosenfeld
- Computer Science
- 19 April 1994
This thesis views language as an information source which emits a stream of symbols from a finite alphabet (the vocabulary), and applies the principle of Maximum Entropy to identify and exploit sources of information in the language stream, so as to minimize its perceived entropy.
Two decades of statistical language modeling: where do we go from here?
- R. Rosenfeld
- Computer ScienceProceedings of the IEEE
- 1 August 2000
A Bayesian approach to integration of linguistic theories with data is argued for inStatistical language models estimate the distribution of various natural language phenomena for the purpose of speech recognition and other language technologies.
A maximum entropy approach to adaptive statistical language modelling
- R. Rosenfeld
- Computer ScienceComputer Speech and Language
- 1 July 1996
An adaptive statistical language model is described, which successfully integrates long distance linguistic information with other knowledge sources, and shows the feasibility of incorporating many diverse knowledge sources in a single, unified statistical framework.
Statistical language modeling using the CMU-cambridge toolkit
- P. Clarkson, R. Rosenfeld
- Computer ScienceEUROSPEECH
- 22 September 1997
The CMU Statistical Language Modeling toolkit was re leased in in order to facilitate the construction and testing of bigram and trigram language models and the technology as implemented in the toolkit is outlined.
Improving Text Classification by Shrinkage in a Hierarchy of Classes
- A. McCallum, R. Rosenfeld, Tom Michael Mitchell, Andrew Y. Ng
- Computer ScienceInternational Conference on Machine Learning
- 24 July 1998
This paper shows that the accuracy of a naive Bayes text classi er can be improved by taking advantage of a hierarchy of classes, and adopts an established statistical technique called shrinkage that smoothes parameter estimates of a data-sparse child with its parent in order to obtain more robust parameter estimates.
The SPHINX-II speech recognition system: an overview
- Xuedong Huang, F. Alleva, H. Hon, M. Hwang, Kai-Fu Lee, R. Rosenfeld
- Physics, Computer ScienceComputer Speech and Language
- 1993
The SPHINX-II speech recognition system is reviewed and recent efforts on improved speech recognition are summarized.
A Maximum Entropy Approach to Adaptive Statistical Language Modeling
- R. Rosenfeld
- Computer Science
- 2001
An adaptive language model based on the principle of Maximum Entropy was trained on the Wall Street Journal corpus, and showed 32%–39% perplexity reduction over the baseline, illustrating the feasibility of incorporating many diverse knowledge sources in a single, unified statistical framework.
A survey of smoothing techniques for ME models
- Stanley F. Chen, R. Rosenfeld
- Computer ScienceIEEE Transactions on Speech and Audio Processing
- 2000
Over a large number of data sets, it is found that fuzzy ME smoothing performs as well as or better than all other algorithms under consideration.
Generating remote control interfaces for complex appliances
- Jeffrey Nichols, B. Myers, Mathilde Pignol
- Computer ScienceACM Symposium on User Interface Software and…
- 27 October 2002
The architecture that supports the PUC is described, and the interface generators that use the specification language to build high-quality graphical and speech interfaces are described.
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