Difference between revisions of "Z. Kou and W. Cohen. SDM 2007"
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== Summary == | == Summary == | ||
| − | This [[Category::paper]] is an extension of [[ | + | This [[Category::paper]] is an extension of [[UsesMethod::Stacked Sequential Learning]] and shows how stacking can be used in non-sequential tasks, such as |
[[AddressesProblem::text region detection]] and [[AddressesProblem::document classification]]. | [[AddressesProblem::text region detection]] and [[AddressesProblem::document classification]]. | ||
| Line 15: | Line 15: | ||
Within their model, the relational information is captured by augmenting data with the predictions of related instances. | Within their model, the relational information is captured by augmenting data with the predictions of related instances. | ||
They applied their method on various problems with different datasets: | They applied their method on various problems with different datasets: | ||
| − | [[UsesDataset::Web KB dataset]], [[UsesDataset::Cora network]], | + | [[UsesDataset::Web KB dataset]], [[UsesDataset::Cora network]], [[UsesDataset::GENIA dataset]], [[UsesDataset::Yapex dataset]]. |
== Example Stacked Graphical Models Usage == | == Example Stacked Graphical Models Usage == | ||
'''text region detection''' | '''text region detection''' | ||
| + | |||
The task is to find the text regions in figures, where each figure may contain several panels. | The task is to find the text regions in figures, where each figure may contain several panels. | ||
| − | + | Relational information comes from the prediction of other candidates within the same panel and the predictions of four neighbor panels (up, down, right and left). | |
| − | + | The predictions of the candidate region and neighbor regions return a vector of binary features indicating whether a character is found in those regions. | |
'''webpage classification''' | '''webpage classification''' | ||
| + | |||
The task is to classify web pages into different categories such as . | The task is to classify web pages into different categories such as . | ||
Relational information comes from the number of incoming and outgoing links in each category. | Relational information comes from the number of incoming and outgoing links in each category. | ||
'''NER''' | '''NER''' | ||
| − | + | ||
| + | Relational information comes from nearby words. In this case Stacked Graphical Model is the same with SSL. | ||
Latest revision as of 13:36, 21 October 2011
Citation
Zhenzhen Kou and William W. Cohen. Stacked Graphical Models for Efficient Inference in Markov Random Fields in SDM-2007.
Online version
Summary
This paper is an extension of Stacked Sequential Learning and shows how stacking can be used in non-sequential tasks, such as text region detection and document classification.
The paper shows their method has very fast inference time, 40 to 80 times faster than Gibbs sampling. Within their model, the relational information is captured by augmenting data with the predictions of related instances. They applied their method on various problems with different datasets: Web KB dataset, Cora network, GENIA dataset, Yapex dataset.
Example Stacked Graphical Models Usage
text region detection
The task is to find the text regions in figures, where each figure may contain several panels. Relational information comes from the prediction of other candidates within the same panel and the predictions of four neighbor panels (up, down, right and left). The predictions of the candidate region and neighbor regions return a vector of binary features indicating whether a character is found in those regions.
webpage classification
The task is to classify web pages into different categories such as . Relational information comes from the number of incoming and outgoing links in each category.
NER
Relational information comes from nearby words. In this case Stacked Graphical Model is the same with SSL.
