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 relation extraction in two ways. First, it automatically determines a dynamic context-sensitive tree span for relation extraction by extending the widely-used Shortest Path-enclosed Tree (SPT) to include necessary context information outside SPT. Second, it proposes a context-sensitive convolution tree kernel, which enumerates both context-free and contextsensitive sub-trees by considering their ancestor node paths as their contexts. Moreover, this paper evaluates the complementary nature between our tree kernel and a state-of-the-art linear kernel. Evaluation on the ACE RDC corpora shows that our dynamic context-sensitive tree span is much more suitable for relation extraction than SPT and our tree kernel outperforms the state-of-the-art Collins and Duffy’s convolution tree kernel. It also shows that our tr...

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Guodong Zhou, Min Zhang, Dong-Hong Ji, Qiaoming Zhu
• Keywords: Convolution Tree Kernel, EMNLP 2007, Natural Language Processing, Relation Extraction, Tree Kernel