  Most current models of word representations(e.g.,GloVe) have successfully
captured fine-grained semantics. However, semantic similarity exhibited in
these word embeddings is not suitable for resolving bridging anaphora, which
requires the knowledge of associative similarity (i.e., relatedness) instead of
semantic similarity information between synonyms or hypernyms. We create word
embeddings (embeddings_PP) to capture such relatedness by exploring the
syntactic structure of noun phrases. We demonstrate that using embeddings_PP
alone achieves around 30% of accuracy for bridging anaphora resolution on the
ISNotes corpus. Furthermore, we achieve a substantial gain over the
state-of-the-art system (Hou et al., 2013) for bridging antecedent selection.
