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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)

- Fred W. Lorey, Pimlak Charoenkwan, +6 authors David H. K. Chui
- British journal of haematology
- 2001

Haemoglobin H (Hb H) disease is caused by deletion or inactivation of three alpha-globin genes, leaving only one intact and active alpha-globin gene. People with Hb H disease usually have moderate anaemia, but are generally thought to be asymptomatic. Some Hb H disease patients require transfusions, and there are reports of fetuses with Hb H disease who… (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)

- Peter F. Brown, John Cocke, +5 authors Paul S. Roossin
- Computational Linguistics
- 1990

The field of machine translation is almost as old as the modern digital computer. In 1949 Warren Weaver suggested that the problem be attacked with statistical methods and ideas from information theory, an area which he, Claude Shannon, and others were developing at the time (Weaver 1949). Although researchers quickly abandoned this approach, advancing… (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
- 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)

- Stephen Della Pietra, Vincent J. Della Pietra, John D. Lafferty
- IEEE Trans. Pattern Anal. Mach. Intell.
- 1997

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 are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the… (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)

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

We present a framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory. The framework suggests an operational retrieval model that extends recent developments in the language modeling approach to information retrieval. A language model for each document is… (More)