@inproceedings{1e45b058c5cd43ad80bda8dbb123f934,
title = "Polynomial semantic indexing",
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.",
author = "Bing Ba and Kunihiko Sadamasa and Jason Weston and Yanjun Qi and David Grangier and Corinna Cortes and Ronan Collobert and Mehryar Mohri",
year = "2009",
language = "English (US)",
isbn = "9781615679119",
series = "Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference",
publisher = "Neural Information Processing Systems",
pages = "64--72",
booktitle = "Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference",
note = "23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 ; Conference date: 07-12-2009 Through 10-12-2009",
}