Probabilistic retrieval based on staged logistic regression
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
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
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
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
Advances in XML Information Retrieval: Third International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2004, Dagstuhl Castle, ... 2004 (Lecture Notes in Computer Science)
Learning Theory: 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings (Lecture Notes in Computer Science)
Automatic text summarization based on word-clusters and ranking algorithms
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Learning Methods in Multi-grained Query Answering
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Progress in information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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We consider the Structured Information Retrieval task which consists in ranking nested textual units according to their relevance for a given query, in a collection of structured documents. We propose to improve the performance of a baseline Information Retrieval system by using a learning ranking algorithm which operates on scores computed from document elements and from their local structural context. This model is trained to optimize a Ranking Loss criterion using a training set of annotated examples composed of queries and relevance judgments on a subset of the document elements. The model can produce a ranked list of documents elements which fulfills a given information need expressed in the query. We analyze the performance of our algorithm on the INEX collection and compare it to a baseline model which is an adaptation of Okapi to Structured Information Retrieval.