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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...
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...
Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This ...
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...
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...
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...