Relation Extraction with Relation Topics
Chang Wang, James Fan, Aditya Kalyanpur, David C. Gondek · 2011
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 detec-tors. Specically, we detect a new semantic relation by projecting the new relation's train-ing instances onto a lower dimension topic space constructed from existing relation de-tectors 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 dened at multiple scales from the re-lation repository to characterize the existing relations. Similar to the topics dened over words, each relation topic is an interpretable multinomial distribution over the existing re-lations. 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 Wikipedia and ACE data have conrmed that background-knowledge-based topics generated from the Wikipedia relation repository can signicantly improve the performance over the state-of-the-art relation detection approaches. 1