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ICTIR
2009
Springer

Robust Word Similarity Estimation Using Perturbation Kernels

9 years 5 months ago
Robust Word Similarity Estimation Using Perturbation Kernels
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.
Kevyn Collins-Thompson
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ICTIR
Authors Kevyn Collins-Thompson
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