Machine learning ranking and INEX'05
Jean-Noel Vittaut and Patrick Gallinari
In: 4th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2005, 28-30 Nov 2005, Dagstuhl Castle, Germany.
We present a Machine Learning based ranking model which can automatically learn its parameters using a training set of annotated examples composed of queries and relevance judgments on a subset of the document elements. Our model improves the performance of a baseline Information Retrieval system by optimizing a ranking loss criterion and combining scores computed from doxels and from their local structural context. We analyze the performance of our algorithm on CO-Focussed and CO-Thourough tasks and compare it to the baseline model which is an adaptation of Okapi to Structured Information Retrieval.