Abstract
We present a class of models that are discriminatively trained to directly map from the word content in a query-document or document- document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, pol- ysemy). 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.
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Bai, B., Weston, J., Collobert, R., Grangier, D. (2009). Supervised Semantic Indexing. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_81
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DOI: https://doi.org/10.1007/978-3-642-00958-7_81
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