Commonsense Knowledge Enhanced Embeddings for Solving Pronoun
  Disambiguation Problems in Winograd Schema Challenge
  In this paper, we propose commonsense knowledge enhanced embeddings (KEE) for
solving the Pronoun Disambiguation Problems (PDP). The PDP task we investigate
in this paper is a complex coreference resolution task which requires the
utilization of commonsense knowledge. This task is a standard first round test
set in the 2016 Winograd Schema Challenge. In this task, traditional linguistic
features that are useful for coreference resolution, e.g. context and gender
information, are no longer effective anymore. Therefore, the KEE models are
proposed to provide a general framework to make use of commonsense knowledge
for solving the PDP problems. Since the PDP task doesn't have training data,
the KEE models would be used during the unsupervised feature extraction
process. To evaluate the effectiveness of the KEE models, we propose to
incorporate various commonsense knowledge bases, including ConceptNet, WordNet,
and CauseCom, into the KEE training process. We achieved the best performance
by applying the proposed methods to the 2016 Winograd Schema Challenge. In
addition, experiments conducted on the standard PDP task indicate that, the
proposed KEE models could solve the PDP problems by achieving 66.7% accuracy,
which is a new state-of-the-art performance.
