A maximum entropy approach to natural language processing
Computational Linguistics
The maximum entropy principle in information retrieval
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring the similarity space
ACM SIGIR Forum
Testing the maximum entropy principle for information retrieval
Journal of the American Society for Information Science
The maximum entropy approach and probabilistic IR models
ACM Transactions on Information Systems (TOIS)
Combining document representations for known-item search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Combining evidence for Web retrieval using the inference network model: an experimental study
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Term proximity scoring for keyword-based retrieval systems
ECIR'03 Proceedings of the 25th European conference on IR research
Retrieval experiments using pseudo-desktop collections
Proceedings of the 18th ACM conference on Information and knowledge management
Evaluating search in personal social media collections
Proceedings of the fifth ACM international conference on Web search and data mining
Foundations and Trends in Information Retrieval
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It is becoming increasingly common in information retrieval to combine evidence from multiple resources to compute the retrieval status value of documents. Although this has led to considerable improvements in several retrieval tasks, one of the outstanding issues is estimation of the respective weights that should be associated with the different sources of evidence. In this paper we propose to use maximum entropy in combination with the limited memory LBFG algorithm to estimate feature weights. Examining the effectiveness of our approach with respect to the known-item finding task of enterprise track of TREC shows that it significantly outperforms a standard retrieval baseline and leads to competitive performance.