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　TREC　collections　show　the　effectiveness　of　our　model　compared　to　the　state-of-The-Art　methods.　©　2019　IEEE.