Unsupervised Entailment Detection between Dependency Graph Fragments
Workshop on Biomedical Natural Language Processing
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Rei, M., & Briscoe, T. (2011). Unsupervised Entailment Detection between Dependency Graph Fragments. Workshop on Biomedical Natural Language Processing. https://doi.org/10.17863/CAM.21358
Entailment detection systems are generally designed to work either on single words, relations or full sentences. We propose a new task – detecting entailment between dependency graph fragments of any type – which relaxes these restrictions and leads to much wider entailment discovery. An unsupervised framework is described that uses intrinsic similarity, multi-level extrinsic similarity and the detection of negation and hedged language to assign a confidence score to entailment relations between two fragments. The final system achieves 84.1% average precision on a data set of entailment examples from the biomedical domain.
This record's DOI: https://doi.org/10.17863/CAM.21358
This record's URL: https://www.repository.cam.ac.uk/handle/1810/274250