  In this paper, we propose a salient-context based semantic matching method to
improve relevance ranking in information retrieval. We first propose a new
notion of salient context and then define how to measure it. Then we show how
the most salient context can be located with a sliding window technique.
Finally, we use the semantic similarity between a query term and the most
salient context terms in a corpus of documents to rank those documents.
Experiments on various collections from TREC show the effectiveness of our
model compared to the state-of-the-art methods.
