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Lecture Notes in Computer Science
We present a class of models that are discriminatively trained to directly map from the word content in a query-document or documentdocument pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained with a supervised signal directly on the task of interest, which we argue is the reason for our superior results. We provide an empirical study on Wikipedia documents, using thedoi:10.1007/978-3-642-00958-7_81 fatcat:ixjpdfiehnejjonvnzclshj76y