  Existing approaches for Chinese zero pronoun resolution overlook semantic
information. This is because zero pronouns have no descriptive information,
which results in difficulty in explicitly capturing their semantic similarities
with antecedents. Moreover, when dealing with candidate antecedents,
traditional systems simply take advantage of the local information of a single
candidate antecedent while failing to consider the underlying information
provided by the other candidates from a global perspective. To address these
weaknesses, we propose a novel zero pronoun-specific neural network, which is
capable of representing zero pronouns by utilizing the contextual information
at the semantic level. In addition, when dealing with candidate antecedents, a
two-level candidate encoder is employed to explicitly capture both the local
and global information of candidate antecedents. We conduct experiments on the
Chinese portion of the OntoNotes 5.0 corpus. Experimental results show that our
approach substantially outperforms the state-of-the-art method in various
experimental settings.
