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Supervised semantic indexing

By Bing Bai, Jason Weston, Ronan Collobert and David Grangier

Abstract

Abstract. 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 the links to define document-document or query-document pairs, where we obtain state-of-the-art performance using our method. Key words: supervised, semantic indexing, document ranking

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.192.9973
Provided by: CiteSeerX
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