2007 TreeKernelRelExtrWithCntxtSensPSTreeInfo

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Abstract

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 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.


References

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Bibtex

@InProceedings{zhou-EtAl:2007:EMNLP-CoNLL2007,

 author    = {Zhou, GuoDong  and Zhang, Min  and Ji, DongHong  and Zhu, QiaoMing},
 title     = {Tree Kernel-based Relation Extraction with Context-Sensitive Structured Parse Tree Information},
 booktitle = {Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)},
 pages     = {728--736},
 url       = {http://www.aclweb.org/anthology/D/D07/D07-1076}

} ,


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2007 TreeKernelRelExtrWithCntxtSensPSTreeInfoMin Zhang
GuoDong Zhou
Dong Hong
Ji Qiaoming Zhu
Tree Kernel-based Relation Extraction with Context-Sensitive Structured Parse Tree InformationProceedings of ACL-2007http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.80.504210.1.1.80.50422007