Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
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Journal of the ACM (JACM)
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IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
Improving semi-supervised acquisition of relation extraction patterns
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
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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.