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 t he 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 tree kernel achieves much better performance than the state-of-the-art linear kernels . Finally, it shows that feature-based and tree kernel-based methods much complement each other and the composite kernel can well integrate both flat and structured features.
Zhou et al. REF proposed a composite of the tree kernel and a linear kernel that outperformed the individual kernels.
Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information
2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)
Computer Science