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The Winograd Schema Challenge (WSC) was proposed by Levesque et al. in 2011 as a new test in Artificial Intelligence (AI) and possibly as an alternative to the Turing test. WSC is a complex coreference resolution task which requires applying knowledge on commonsense reasoning. It is an easy task for humans but it still remains an unsolved challenge for computers. There are two categories of proposed approaches for tackling the WSC. The first encompasses techniques based on formalized Knowledge Representation and Reasoning (KRR), while the second entails Machine Learning (ML) approaches. In this paper we provide a review of the state-of-the-art approaches proposed from both categories and we outline their strengths and weaknesses. Additionally, we discuss a recent work which combines techniques from both categories as it seems to be a promising and innovative approach.
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