2004 DependencyTreeKernelsForRelationExtraction

• (Culotta & Sorensen, 2004) ⇒ Aron Culotta, and Jeffrey S. Sorensen. (2004). “Dependency Tree Kernels for Relation Extraction.” In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004). doi:10.3115/1218955.1219009

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

Cited By

• ~315 http://scholar.google.com/ …

2007

• (Banko et al., 2007) ⇒ Michele Banko, Michael J. Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni. (2007). “Open Information Extraction from the Web.” In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2007).

2006

• (Zhang et al., 2006) ⇒ M. Zhang, J. Zhang, and J. Su. (2006). “Exploring Syntactic Features for Relation Extraction using a Convolution Tree Kernel.” In: Proceedings of HLT-2006.
  • QUOTE: Culotta and Sorensen (2004) generalize this kernel to estimate similarity between dependency trees. One may note that their tree kernel requires the matchable nodes must be at the same depth counting from the root node. 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 on the ACE corpus (Zhao and Grishman, 2005). In addition, according to the top-down node matching mechanism of the kernel, once a node is not matchable with any node in the same layer in another tree, all the sub-trees below this node are discarded even if some of them are matchable to their counterparts in another tree.
• (Culotta et al., 2006) ⇒ Aron Culotta, Andrew McCallum, and Jonathan Betz. (2006). “Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text.” In: Proceedings of HLT-NAACL Conference (HLT-NAACL 2006). doi:10.3115/1220835.1220873

2005

• (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.

Quotes

Abstract

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-words” kernel.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 DependencyTreeKernelsForRelationExtraction	Aron Culotta Jeffrey S. Sorensen			Dependency Tree Kernels for Relation Extraction			http://www.cs.umass.edu/~culotta/pubs/culotta04dependency.pdf	10.3115/1218955.1219009