366 citations · 950 across the 42 of their papers we have counts for
5 papers · 1 filter
Learning with Scope, with Application to Information Extraction and Classification
David Blei, J Andrew Bagnell, Andrew McCallum
In probabilistic approaches to classification and information extraction, one typically builds a statistical model of words under the assumption that future data will exhibit the s…
Efficiently Inducing Features of Conditional Random Fields
Andrew McCallum
Conditional Random Fields (CRFs) are undirected graphical models, a special case of which correspond to conditionally-trained finite state machines. A key advantage of these models…
An Integrated, Conditional Model of Information Extraction and Coreference with Applications to Citation Matching
Ben Wellner, Andrew McCallum, Fuchun Peng +1
Although information extraction and coreference resolution appear together in many applications, most current systems perform them as ndependent steps. This paper describes an appr…
Piecewise Training for Undirected Models
Charles Sutton, Andrew McCallum
For many large undirected models that arise in real-world applications, exact maximumlikelihood training is intractable, because it requires computing marginal distributions of the…
A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance
Andrew McCallum, Kedar Bellare, Fernando Pereira
The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discrim…