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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)

Recent approaches to text classification have used two different first-order probabilistic models for classification , both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others… (More)

- Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom M. Mitchell
- Machine Learning
- 2000

This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. We… (More)

- Olivier Chapelle, Bernhard Schölkopf, +6 authors Tom Mitchell
- 2007

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.

- Andrew McCallum, Dayne Freitag, Fernando Pereira
- ICML
- 2000

Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. In these cases, the observations are usually mod-eled as multinomial distributions over a discrete vocabulary, and the HMM… (More)

- Charles A. Sutton, Andrew McCallum
- Foundations and Trends in Machine Learning
- 2012

R in sample Vol. xx, No xx (xxxx) 1–87 c xxxx xxxxxxxxx DOI: xxxxxx Abstract Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graph-ical modeling, combining the ability of graphical models to compactly model… (More)

- Xuerui Wang, Andrew McCallum
- KDD
- 2006

This paper presents an LDA-style topic model that captures not only the low-dimensional structure of data, but also how the structure changes over time. Unlike other recent work that relies on Markov assumptions or discretization of time, here each topic is associated with a continuous distribution over timestamps, and for each generated document, the… (More)

Latent variable models have the potential to add value to large document collections by discovering interpretable, low-dimensional subspaces. In order for people to use such models, however, they must trust them. Unfortunately , typical dimensionality reduction methods for text, such as latent Dirichlet allocation , often produce low-dimensional sub-spaces… (More)

- Limin Yao, David M. Mimno, Andrew McCallum
- KDD
- 2009

Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of training documents requires approximate inference techniques that are computationally expensive. With today's large-scale, constantly expanding document collections, it is useful… (More)

- Nicholas Roy, Andrew McCallum
- ICML
- 2001

This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation of the expected future error is intractable. Our approach is made feasible… (More)