کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
515914 | 867139 | 2011 | 13 صفحه PDF | دانلود رایگان |
In this paper, we address the problem of relation extraction of multiple arguments where the relation of entities is framed by multiple attributes. Such complex relations are successfully extracted using a syntactic tree-based pattern matching method. While induced subtree patterns are typically used to model the relations of multiple entities, we argue that hard pattern matching between a pattern database and instance trees cannot allow us to examine similar tree structures. Thus, we explore a tree alignment-based soft pattern matching approach to improve the coverage of induced patterns. Our pattern learning algorithm iteratively searches the most influential dependency tree patterns as well as a control parameter for each pattern. The resulting method outperforms two baselines, a pairwise approach with the tree-kernel support vector machine and a hard pattern matching method, on two standard datasets for a complex relation extraction task.
Research highlights
► A local tree alignment algorithm is utilized for soft pattern matching.
► The soft pattern matching outperforms hard pattern matching.
► Pattern matching is appropriate for relation extraction of multiple arguments.
Journal: Information Processing & Management - Volume 47, Issue 4, July 2011, Pages 593–605