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ACL
2010
8 years 10 months ago
Convolution Kernel over Packed Parse Forest
This paper proposes a convolution forest kernel to effectively explore rich structured features embedded in a packed parse forest. As opposed to the convolution tree kernel, the p...
Min Zhang, Hui Zhang, Haizhou Li
TASLP
2008
134views more  TASLP 2008»
8 years 12 months ago
Semantic Role Labeling Using a Grammar-Driven Convolution Tree Kernel
Convolution tree kernel has shown promising results in semantic role labeling (SRL). However, this kernel does not consider much linguistic knowledge in kernel design and only perf...
Min Zhang, Wanxiang Che, Guodong Zhou, AiTi Aw, Ch...
IPM
2008
159views more  IPM 2008»
9 years 14 hour ago
Exploring syntactic structured features over parse trees for relation extraction using kernel methods
Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This ...
Min Zhang, Guodong Zhou, AiTi Aw
ACL
2006
9 years 1 months ago
A Hybrid Convolution Tree Kernel for Semantic Role Labeling
A hybrid convolution tree kernel is proposed in this paper to effectively model syntactic structures for semantic role labeling (SRL). The hybrid kernel consists of two individual...
Wanxiang Che, Min Zhang, Ting Liu, Sheng Li
EMNLP
2007
9 years 1 months ago
Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information
This paper proposes a tree kernel with contextsensitive structured parse tree information for relation extraction. It resolves two critical problems in previous tree kernels for r...
Guodong Zhou, Min Zhang, Dong-Hong Ji, Qiaoming Zh...
ACL
2007
9 years 1 months ago
A Grammar-driven Convolution Tree Kernel for Semantic Role Classification
Convolution tree kernel has shown promising results in semantic role classification. However, it only carries out hard matching, which may lead to over-fitting and less accurate s...
Min Zhang, Wanxiang Che, AiTi Aw, Chew Lim Tan, Gu...
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