Relation Extraction with Relation Topics

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Relation Extraction with Relation Topics
This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from previously trained relation detectors. Specifically, we detect a new semantic relation by projecting the new relation’s training instances onto a lower dimension topic space constructed from existing relation detectors through a three step process. First, we construct a large relation repository of more than 7,000 relations from Wikipedia. Second, we construct a set of non-redundant relation topics defined at multiple scales from the relation repository to characterize the existing relations. Similar to the topics defined over words, each relation topic is an interpretable multinomial distribution over the existing relations. Third, we integrate the relation topics in a kernel function, and use it together with SVM to construct detectors for new relations. The experimental results on Wikipedi...
Chang Wang, James Fan, Aditya Kalyanpur, David Gon
Added 20 Dec 2011
Updated 20 Dec 2011
Type Journal
Year 2011
Authors Chang Wang, James Fan, Aditya Kalyanpur, David Gondek
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