A shortest path dependency kernel for relation extraction

ABSTRACT

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.

References

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• Computing methodologies

  • Artificial intelligence

• Hardware

  • Power and energy

    • Power estimation and optimization