Abstract
We introduce perturbation kernels, a new class of similarity measure for information retrieval that casts word similarity in terms of multi-task learning. Perturbation kernels model uncertainty in the user’s query by choosing a small number of variations in the relative weights of the query terms to build a more complete picture of the query context, which is then used to compute a form of expected distance between words. Our approach has a principled mathematical foundation, a simple analytical form, and makes few assumptions about the underlying retrieval model, making it easy to apply in a broad family of existing query expansion and model estimation algorithms.
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References
Ando, R.K., Dredze, M., Zhang, T.: TREC 2005 genomics track experiments at IBM Watson. In: Proceedings of TREC 2005, NIST Special Publication (2006)
Ando, R.K., Zhang, T.: A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learning Research 6, 1817–1853 (2005)
Baxter, J.: The canonical distortion measure for vector quantization and function approximation. In: ICML 1997, pp. 39–47 (1997)
Collins-Thompson, K.: Estimating robust query models using convex optimization. In: Advances in Neural Information Processing Systems (NIPS), vol. 21, pp. 329–336. MIT Press, Cambridge (2008)
Collins-Thompson, K.: Robust Model Estimation Methods for Information Retrieval, PhD thesis. Carnegie Mellon University (2008)
Dillon, J., Mao, Y., Lebanon, G., Zhang, J.: Statistical translation, heat kernels, and expected distances. In: UAI 2007, pp. 93–100 (2007)
Jaakkola, T., Haussler, D.: Exploiting generative models in discriminative classifiers. In: Advances in Neural Information Processing Systems (NIPS), vol. 11, pp. 487–493. MIT Press, Cambridge (1999)
Jebara, T., Kondor, R., Howard, A.: Probability product kernels. J. Machine Learning Research 5, 819–844 (2004)
Lafferty, J.D., Lebanon, G.: Information diffusion kernels. In: Advances in Neural Information Processing Systems (NIPS), vol. 15, pp. 375–382. MIT Press, Cambridge (2002)
Lavrenko, V.: A Generative Theory of Relevance. PhD thesis, Univ. of Massachusetts, Amherst (2004)
Minka, T.: Distance measures as prior probabilities. Technical report (2000)
Sahami, M., Heilman, T.: A web-based kernel function for measuring the similarity of short text snippets. In: Proc. of WWW 2006, pp. 377–386 (2006)
Tsuda, K., Kawanabe, M.: The leave-one-out kernel. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 727–732. Springer, Heidelberg (2002)
Tsuda, K., Kin, T., Asai, K.: Marginalized kernels for biological sequences. Bioinformatics 18(1), 268–275 (2002)
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Collins-Thompson, K. (2009). Robust Word Similarity Estimation Using Perturbation Kernels. In: Azzopardi, L., et al. Advances in Information Retrieval Theory. ICTIR 2009. Lecture Notes in Computer Science, vol 5766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04417-5_25
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DOI: https://doi.org/10.1007/978-3-642-04417-5_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04416-8
Online ISBN: 978-3-642-04417-5
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