Unsupervised Entailment Detection between Dependency Graph Fragments
Abstract
Description
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.