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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)

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)

- 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)

- 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)

- Sebastian Riedel, Limin Yao, Andrew McCallum, Benjamin M. Marlin
- HLT-NAACL
- 2013

Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing struc-tured sources of the same schema. The need for existing datasets can be avoided by using a universal schema: the union of all involved… (More)

- Hanna M. Wallach, David M. Mimno, Andrew McCallum
- NIPS
- 2009

Implementations of topic models typically use symmetric Dirichlet priors with fixed concentration parameters, with the implicit assumption that such " smoothing parameters " have little practical effect. In this paper, we explore several classes of structured priors for topic models. We find that an asymmetric Dirichlet prior over the document–topic… (More)

- Andrew McCallum, Wei Li
- CoNLL
- 2003

- Sebastian Riedel, Limin Yao, Andrew McCallum
- ECML/PKDD
- 2010

Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the assumption that each sentence which mentions the two related… (More)