Learning Ranking vs. Modeling Relevance

The classical (ad hoc) document retrieval problem has been traditionally approached through ranking according to heuristically developed functions (such as tf.idf or bm25) or generative language modeling, which requires explicit assumptions about the term distributions. The nowadays popular discriminative (classification, machine learning, statistical forecasting etc.) approaches have been mostly abandoned while solving this task in spite of their success in a different task of text categorization. In this paper, we studied if a classifier can be trained solely based on labeled examples to successfully generalize to new (unseen by the system) queries and provide performance comparable with popular heuristic or language models. Our SVM-based classifier learns from the relevance judgments available with the standard test collections and generalizes to new, previously unseen queries its ability to compare and rank documents with respect to a given query. To accomplish this, we have desig...

• Type: Conference
• Year: 2006
• Where: HICSS
• Authors: Dmitri Roussinov, Weiguo Fan
• Keywords: Biometrics, Document Retrieval Problem, HICSS 2006, Machine Learning, System Sciences, Test Collections