2004 DependencyTreeKernelsForRelationExtraction

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Subject Headings: Relation Recognition from Text Algorithm, ACE Benchmark Task, Tree Kernel.


Cited By




  • (Zhao & Grishman, 2005) ⇒ S. Zhao and Ralph Grishman. (2005). “Extracting Relations with Integrated Information Using Kernel Methods.” In: Proceedings of or ACL-2005.
    • QUOTE: Culotta and Sorensen (2004) described a slightly generalized version of this kernel based on dependency trees. Since their kernel is a recursive match from the root of a dependency tree down to the leaves where the entity nodes reside, a successful match of two relation examples requires their entity nodes to be at the same depth of the tree. This is a strong constraint on the matching of syntax so it is not surprising that the model has good precision but very low recall. In their solution a bag-of-words kernel was used to compensate for this problem. In our approach, more flexible kernels are used to capture regularization in syntax, and more levels of syntactic information are considered.



We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. Using this kernel within a Support Vector Machine, we detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles. We examine the utility of different features such as Wordnet hypernyms, parts of speech, and entity types, and find that the dependency tree kernel achieves a 20% F1 improvement over a “bag-of-wordskernel.


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2004 DependencyTreeKernelsForRelationExtractionAron Culotta
Jeffrey S. Sorensen
Dependency Tree Kernels for Relation ExtractionProceedings of the 42nd Annual Meeting of the Association for Computational Linguisticshttp://www.cs.umass.edu/~culotta/pubs/culotta04dependency.pdf10.3115/1218955.12190092004