Pseudo-relevance feedback is an effective technique for improving retrieval results. Traditional feedback algorithms use a whole feedback document as a unit to extract words for query expansion, which is not optimal as a document may cover several different topics and thus contain much irrelevant information. In this paper, we study how to effectively select from feedback documents those words that are focused on the query topic based on positions of terms in feedback documents. We propose a positional relevance model (PRM) to address this problem in a unified probabilistic way. The proposed PRM is an extension of the relevance model to exploit term positions and proximity so as to assign more weights to words closer to query words based on the intuition that words closer to query words are more likely to be related to the query topic. We develop two methods to estimate PRM based on different sampling processes. Experiment results on two large retrieval datasets show that the proposed PRM is effective and robust for pseudo-relevance feedback, significantly outperforming the relevance model in both document-based feedback and passage-based feedback.