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Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TLDR
This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data. Expand
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
TLDR
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model, and methods to incorporate class priors and the predictions of classifiers obtained by supervised learning are discussed. Expand
Dynamic topic models
TLDR
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections, and dynamic topic models provide a qualitative window into the contents of a large document collection. Expand
A Statistical Approach to Machine Translation
TLDR
The application of the statistical approach to translation from French to English and preliminary results are described and the results are given. Expand
A study of smoothing methods for language models applied to information retrieval
TLDR
Evaluation on five different databases and four types of queries indicates that the two-stage smoothing method with the proposed parameter estimation methods consistently gives retrieval performance that is close to or better than the best results achieved using a single smoothing methods and exhaustive parameter search on the test data. Expand
A study of smoothing methods for language models applied to Ad Hoc information retrieval
TLDR
This paper examines the sensitivity of retrieval performance to the smoothing parameters and compares several popular smoothing methods on different test collections. Expand
Correlated Topic Models
TLDR
The correlated topic model (CTM) is developed, where the topic proportions exhibit correlation via the logistic normal distribution and a mean-field variational inference algorithm is derived for approximate posterior inference in this model, which is complicated by the fact that the Logistic normal is not conjugate to the multinomial. Expand
A correlated topic model of Science
TLDR
The correlated topic model (CTM) is developed, where the topic proportions exhibit correlation via the logistic normal distribution, and it is demonstrated its use as an exploratory tool of large document collections. Expand
Inducing Features of Random Fields
TLDR
The random field models and techniques introduced in this paper differ from those common to much of the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated. Expand
Model-based feedback in the language modeling approach to information retrieval
TLDR
This paper proposes and evaluates two different approaches to updating a query language model based on feedback documents, one based on a generative probabilistic model of feedback documents and onebased on minimization of the KL-divergence over feedback documents. Expand
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