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Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
We present conditional random fields , a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov modelsExpand
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Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weightsExpand
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Dynamic topic models
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters ofExpand
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A Statistical Approach to Machine Translation
In this paper, we present a statistical approach to machine translation. We describe the application of our approach to translation from French to English and give preliminary results.
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A study of smoothing methods for language models applied to information retrieval
Language modeling approaches to information retrieval are attractive and promising because they connect the problem of retrieval with that of language model estimation, which has been studiedExpand
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Correlated Topic Models
Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of eachExpand
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Inducing Features of Random Fields
We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that areExpand
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A correlated topic model of Science
Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of eachExpand
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Model-based feedback in the language modeling approach to information retrieval
The language modeling approach to retrieval has been shown to perform well empirically. One advantage of this new approach is its statistical foundations. However, feedback, as one importantExpand
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High-dimensional Ising model selection using ℓ1-regularized logistic regression
We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhoodExpand
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