Polynomial semantic indexing

Bing Ba, Kunihiko Sadamasa, Jason Weston, Yanjun Qi, David Grangier, Corinna Cortes, Ronan Collobert, Mehryar Mohri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Dealing with polynomial models on word features is computationally challenging. We propose a low-rank (but diagonal preserving) representation of our polynomial models to induce feasible memory and computation requirements. We provide an empirical study on retrieval tasks based on Wikipedia documents, where we obtain state-of-the-art performance while providing realistically scalable methods.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
Pages64-72
Number of pages9
StatePublished - 2009
Event23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 - Vancouver, BC, Canada
Duration: Dec 7 2009Dec 10 2009

Publication series

NameAdvances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Other

Other23rd Annual Conference on Neural Information Processing Systems, NIPS 2009
CountryCanada
CityVancouver, BC
Period12/7/0912/10/09

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Ba, B., Sadamasa, K., Weston, J., Qi, Y., Grangier, D., Cortes, C., Collobert, R., & Mohri, M. (2009). Polynomial semantic indexing. In Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference (pp. 64-72). (Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference).