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- John D. Lafferty, Andrew McCallum, Fernando Pereira
- ICML
- 2001

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 models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid… (More)

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 weights encoding the similarity between instances. The learning problem is then formulated in terms of a Gaussian random field on this graph, where the mean of the field is… (More)

- David M. Blei, John D. Lafferty
- ICML
- 2006

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 of the multinomial distributions that represent the topics. Variational approximations based on Kalman filters and nonparametric wavelet regression are developed… (More)

- ChengXiang Zhai, John D. Lafferty
- SIGIR Forum
- 2001

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 studied extensively in other application areas such as speech recognition. The basic idea of these approaches is to estimate a language model for each document, and then rank… (More)

- ChengXiang Zhai, John D. Lafferty
- ACM Trans. Inf. Syst.
- 2004

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 studied extensively in other application areas such as speech recognition. The basic idea of these approaches is to estimate a language model for each document, and to then… (More)

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 each document arise from a mixture of topics, each of which is a distribution over the vocabulary. A limitation of LDA is the inability to model topic correlation… (More)

- Risi Kondor, John D. Lafferty
- ICML
- 2002

The application of kernel-based learning algorithms has, so far, largely been confined to realvalued data and a few special data types, such as strings. In this paper we propose a general method of constructing natural families of kernels over discrete structures, based on the matrix exponentiation idea. In particular, we focus on generating kernels on… (More)

- ChengXiang Zhai, John D. Lafferty
- CIKM
- 2001

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 important component in a retrieval system, has only been dealt with heuristically in this new retrieval approach: the original query is usually literally expanded by adding… (More)

- David M. Blei, John D. Lafferty
- NIPS
- 2005

Topic models, such as latent Dirichlet allocation (LDA), have been an effective tool for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of each document arise from a mixture of topics, each of which is a distribution over the vocabulary. A limitation of LDA is the inability to model topic… (More)

We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on 1-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an 1-constraint. The method is analyzed under high-dimensional scaling in which both the… (More)