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In this paper, we propose commonsense knowledge enhanced embeddings (KEE) forsolving the Pronoun Disambiguation Problems (PDP). The PDP task we investigatein this paper is a complex coreference resolution task which requires theutilization of commonsense knowledge. This task is a standard first round testset in the 2016 Winograd Schema Challenge. In this task, traditional linguisticfeatures that are useful for coreference resolution, e.g. context and genderinformation, are no longer effective anymore. Therefore, the KEE models areproposed to provide a general framework to make use of commonsense knowledgefor solving the PDP problems. Since the PDP task doesn't have training data,the KEE models would be used during the unsupervised feature extractionprocess. To evaluate the effectiveness of the KEE models, we propose toincorporate various commonsense knowledge bases, including ConceptNet, WordNet,and CauseCom, into the KEE training process. We achieved the best performanceby applying the proposed methods to the 2016 Winograd Schema Challenge. Inaddition, experiments conducted on the standard PDP task indicate that, theproposed KEE models could solve the PDP problems by achieving 66.7% accuracy,which is a new state-of-the-art performance.

Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge