Abstract
Prior work has investigated the problem mining arguments from online reviews by classifying opinions based on the stance expressed explicitly or implicitly. An implicit opinion has the stance left unexpressed linguistically while an explicit opinion has the stance expressed explicitly. In this paper, we propose a bipartite graph-based approach to relate a given set of explicit opinions as simplified arguments for a given set of implicit opinions using three different features (a) sentence similarity, (b) sentiment and (c) target. Experiments are carried out on a manually annotated set of explicit-implicit opinions and show that unsupervised sentence representations can be used to accurately match arguments with their corresponding simplified versions.
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