Tree Structured CRF Model
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A tree structured CRF model is a CRF model that makes use of a tree data structure.
- AKA: TCRF, Tree CRF, Tree Structured CRF, Tree Structured Conditional Random Field Model.
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- Counter-Example(s):
- See: Structured CRF Model.
References
2007
- (Reynolds et al., 2007) ⇒ Jordan Reynolds, and Kevin Murphy. (2007). “Figure-ground Segmentation Using a Hierarchical Conditional Random Field.” In: Proceedings of the Fourth Canadian Conference on Computer and Robot Vision. doi:10.1109/CRV.2007.32
- QUOTE: Our system combines the regions together into a hierarchical, tree-structured conditional random field, applies the classifier to each node (region), and fuses all the information together using belief propagation.
2006
- (Tang et al., 2006) ⇒ Jie Tang, Mingcai Hong, Juanzi Li, and Bangyong Liang. (2006). “Tree-Structured Conditional Random Fields for Semantic Annotation.” In: Proceedings of the Semantic Web conference (ISWC 2006). doi:10.1007/11926078
- QUOTE: ... This paper is concerned with semantic annotation on hierarchically dependent data (hierarchical semantic annotation). We propose a Tree-structured Conditional Random Field (TCRF) model to better incorporate dependencies across the hierarchically laid-out information. Methods for performing the tasks of model-parameter estimation and annotation in TCRFs have been proposed. Experimental results indicate that the proposed TCRFs for hierarchical semantic annotation can significantly outperform the existing linear-chain CRF model.
2005
- (Cohn et al., 2005) ⇒ Trevor Cohn, and Philip Blunsom. (2005). “Semantic Role Labelling with Tree Conditional Random Fields.” In: Proceedings of the Ninth Conference on Computational Natural Language Learning.