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We propose a framework for searching the Wikipedia with contextual information. Our framework extends the typical keyword search, by considering queries of the type (q,p), where q is a set of terms (as in classical Web search), and p is a source Wikipedia document. The query terms q represent the information that the user is interested in finding, and the document p provides the context of the query. The task is to rank other documents in Wikipedia with respect to their relevance to the query terms q given the context document p. By associating a context to the query terms, the search results of a search initiated in a particular page can be made more relevant. We suggest a number of features that extend the classical query-search model so that the context document p is considered. We then use RankSVM (Joachims 2002) to learn weights for the individual features given suitably constructed training data. Documents are ranked at query time using the inner product of the feature and the weight vectors. The experiments indicate that the proposed method considerably improves results obtained by a more traditional approach that does not take the context into account.