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.
REF point out that both SPT and the convolution tree kernel are context-free.
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