Supervised semantic indexing

13 years 9 months ago
Supervised semantic indexing
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
Bing Bai, Jason Weston, David Grangier, Ronan Coll
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where CIKM
Authors Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Olivier Chapelle, Kilian Q. Weinberger
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