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Highly Cited

2010

Highly Cited

2010

In dyadic prediction, labels must be predicted for pairs (dyads) whose members possess unique identifiers and, sometimes… Expand

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Highly Cited

2010

Highly Cited

2010

We describe a method of incorporating task-specific cost functions into standard conditional log-likelihood (CLL) training of… Expand

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Highly Cited

2007

Highly Cited

2007

The L-BFGS limited-memory quasi-Newton method is the algorithm of choice for optimizing the parameters of large-scale log-linear… Expand

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Highly Cited

2007

Highly Cited

2007

This article describes a number of log-linear parsing models for an automatically extracted lexicalized grammar. The models are… Expand

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Highly Cited

2006

Highly Cited

2006

When training the parameters for a natural language system, one would prefer to minimize 1-best loss (error) on an evaluation set… Expand

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Highly Cited

2005

Highly Cited

2005

Conditional random fields (Lafferty et al., 2001) are quite effective at sequence labeling tasks like shallow parsing (Sha and… Expand

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Highly Cited

1997

Highly Cited

1997

The primary focus here is on log-linear models for contingency tables, but in this second edition, greater emphasis has been… Expand

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Highly Cited

1997

Highly Cited

1997

Introduction Log-Linear Anaylsis Log-Linear Anaylsis with Latent Variables and Missing Data Event History Analysis Event History… Expand

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Highly Cited

1992

Highly Cited

1992

Analysis of data from 280 rivers discharging to the ocean indicates that sediment loads/yields are a log-linear function of basin… Expand

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Highly Cited

1980

Highly Cited

1980

Discusses the innovative log-linear model of statistical analysis. This model makes no distinction between independent and… Expand

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