The surge of misinformation poses a serious problem for fact-checkers. Several initiatives for manualfact-checking have stepped up to combat this ordeal. However, computational methods are needed tomake the verification faster and keep up with the increasing abundance of false information. MachineLearning (ML) approaches have been proposed as a tool to ease the work of manual fact-checking.Specifically, the act of checking textual claims by using relational datasets has recently gained a lot oftraction. However, despite the abundance of proposed solutions, there has not been any formal definitionof the problem, nor a comparison across the different assumptions and results. In this work, we makea first attempt at solving these ambiguities. First, we formalize the problem by providing a generaldefinition that is applicable to all systems and that is agnostic to their assumptions. Second, we definegeneral dimensions to characterize different prominent systems in terms of assumptions and features.Finally, we report experimental results over three scenarios with corpora of real-world textual claims.