This paper studies document ranking under uncertainty. It is tackled in a general situation where the relevance predictions of individual documents have uncertainty, and are depen...
We describe a method for applying parsimonious language models to re-estimate the term probabilities assigned by relevance models. We apply our method to six topic sets from test ...
Edgar Meij, Wouter Weerkamp, Krisztian Balog, Maar...
Due to both the size and growth of the internet, new tools are needed to assist with the finding and extraction of very specific resources relevant to a user's task. Previous...
We approached the problem as learning how to order documents by estimated relevance with respect to a user query. Our support vector machines based classifier learns from the rele...
Dmitri Roussinov, Weiguo Fan, Fernando A. Das Neve...
This paper describes the baselines proposed for the ResPubliQA 2009 task. These baselines are purely based on information retrieval techniques. The selection of an adequate retrie...