Automatic combination of multiple ranked retrieval systems
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Relevance weighting for query independent evidence
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking and scoring using empirical risk minimization
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Automatic text summarization based on word-clusters and ranking algorithms
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
An algebra for structured queries in bayesian networks
INEX'04 Proceedings of the Third international conference on Initiative for the Evaluation of XML Retrieval
XML search: languages, INEX and scoring
ACM SIGMOD Record
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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.