We present a novel approach to relation extraction, based on the observation that the information required to assert a relationship between two named entities in the same sentence is typically captured by the shortest path between the two entities in the dependency graph. Experiments on extracting top-level relations from the ACE (Automated Content Extraction) newspaper corpus show that the new shortest path dependency kernel outperforms a recent approach based on dependency tree kernels.
Our work is also related to REF , where the similarity between the words on the path connecting two entities in the dependency graph is used to devise a Kernel function.
A Shortest Path Dependency Kernel For Relation Extraction
Human Language Technology Conference And Empirical Methods In Natural Language Processing
Computer Science