When its support set is {(Bush, China), (Mandelson, Moscow), (Rice, Israel)}, and that of visit is {(Bush, China), (Rice, Israel), (Medvedev, Cuba)}, we can infer their semantic equivalence based on the set intersection: In contrast, we propose to explore corpus latent features (LF), to complement the sparsity problem of EF: Out of 158 randomly chosen correct relation translation pairs we labeled, 64% has only one co-occurring entity pair, which makes EF not very effective to identify these relation translations.
note that the absolute numbers (due to the harsh evaluation criteria) may look low.