Web tables are a valuable source of information used in many application areas. However, to exploit Web tables it is necessary to understand their content and intention which is impeded by their ambiguous semantics and inconsistencies. Therefore, additional context information, e.g.Text in which the tables are embedded, is needed to support the table understanding process. In this paper, we propose a novel contextualization approach that 1) splits the table context in topically coherent paragraphs, 2) provides a similarity measure that is able to match each paragraph to the table in question and 3) ranks these paragraphs according to their relevance. Each step is accompanied by an experimental evaluation on real-world data showing that our approach is feasible and effectively identifies the most relevant context for a given Web table.