Tree Kernel Function
A tree kernel function is a kernel function for Data that is represented as Trees.
- AKA: Tree Kernel.
- Context:
- It can be induced by a Tree Kernel Function Induction Algorithm.
- See: Relational Data Kernel Function, Vector Kernel Function, Graph Kernel Function.
References
2010
- (Pighin, 2010) ⇒ Daniele Pighin. (2010). “Greedy Feature Selection in Tree Kernel Spaces." PhD Thesis. DISI - University of Trento
- QUOTE: A tree kernel is a convolution kernel (Haussler, 1999) defined over tree pairs, i.e. a kernel that evaluates the similarity between two trees by estimating the degree of their overlap.
2006
- (Moschitti, 2006a) ⇒ Alessandro Moschitti. (2006). “Making Tree Kernels Practical for Natural Language Learning.” In: Proceedings of ECML 2006.
- (Moschitti et al., 2006a) ⇒ Alessandro Moschitti, Daniele Pighin, and Roberto Basili. (2006). “Tree Kernel Engineering for Proposition Re-ranking.” In: Proceedings of ECML-PKDD 2006 Workshop on Mining and Learning with Graphs (MLG 2006).
- (KuboyamaSK, 2006) ⇒ Tetsuji Kuboyama, Kilho Shin, and Hisashi Kashima. (2006). Flexible Tree Kernels based on Counting the Number of Tree Mappings. ECML/PKDD Workshop on Mining and Learning with Graphs.
2004
- (Culotta & Sorensen, 2004) ⇒ Aron Culotta, and Jeffrey S. Sorensen. (2004). “Dependency Tree Kernels for Relation Extraction.” In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004). doi:10.3115/1218955.1219009
- QUOTE: We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences.
2001
- (Collins and Duffy, 2001) ⇒ Michael Collins and N. Duffy. (2001). “Convolution Kernels for Natural Language.” In: Proceedings of NIPS-2001.
1999
- (Haussler, 1999) ⇒ David Haussler. (1999). “Convolution Kernels on Discrete Structures." Technical Report UCSC-CLR-99-10, University of California at Santa Cruz.