A Shortest Path Dependency Kernel for Relation Extraction

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 724–731, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics A Shortest Path Dependency Kernel for Relation Extraction Razvan C. Bunescu and Raymond J. Mooney Department of Computer Sciences University of Texas at Austin 1 University Station C0500 Austin, TX 78712 razvan,mooney@cs.utexas.edu Abstract We present a novel approach to relation extraction, based on the observation that the information required to assert a rela- tionship between two named entities in the same sentence is typically captured by the shortest path between the two en- tities in the dependency graph. Exper- iments on extracting top-level relations from the ACE (Automated Content Ex- traction) newspaper corpus show that the new shortest path dependency kernel out- performs a recent approach based on de- pendency tree kernels. 1 Introduction One of the key tasks in natural language process- ing is that of Information Extraction (IE), which is traditionally divided into three subproblems: coref- erence resolution, named entity recognition, and relation extraction. Consequently, IE corpora are typically annotated with information corresponding to these subtasks (MUC (Grishman, 1995), ACE (NIST, 2000)), facilitating the development of sys- tems that target only one or a subset of the three problems. In this paper we focus exclusively on ex- tracting relations between predefined types of en- tities in the ACE corpus. Reliably extracting re- lations between entities in natural-language docu- ments is still a difficult, unsolved problem, whose inherent difficulty is compounded by the emergence of new application domains, with new types of nar- rative that challenge systems developed for previous well-studied domains. The accuracy level of cur- rent syntactic and semantic parsers on natural lan- guage text from different domains limit the extent to which syntactic and semantic information can be used in real IE systems. Nevertheless, various lines of work on relation extraction have shown experi- mentally that the use of automatically derived syn- tactic information can lead to significant improve- ments in extraction accuracy. The amount of syntac- tic knowledge used in IE systems varies from part- of-speech only (Ray and Craven, 2001) to chunking (Ray and Craven, 2001) to shallow parse trees (Ze- lenko et al., 2003) to dependency trees derived from full parse trees (Culotta and Sorensen, 2004). Even though exhaustive experiments comparing the per- formance of a relation extraction system based on these four levels of syntactic information are yet to be conducted, a reasonable assumption is that the ex- traction accuracy increases with the amount of syn- tactic information used. The performance however depends not only on the amount of syntactic infor- mation, but also on the details of the exact models using this information. Training a machine learn- ing system in a setting where the information used for representing the examples is only partially rele- vant to the actual task often leads to overfitting. It is therefore important to design the IE system so that the input data is stripped of unnecessary features as much as …

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