A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Conceptual information retrieval
SIGIR '80 Proceedings of the 3rd annual ACM conference on Research and development in information retrieval
Exploiting syntactic structure of queries in a language modeling approach to IR
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Syntagmatic and paradigmatic representations of term variation
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Knowledge-intensive conceptual retrieval and passage extraction of biomedical literature
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Overview of the ImageCLEFmed 2007 Medical Retrieval and Medical Annotation Tasks
Advances in Multilingual and Multimodal Information Retrieval
Multiplying Concept Sources for Graph Modeling
Advances in Multilingual and Multimodal Information Retrieval
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
MRIM-LIG at ImageCLEF 2009: robotvision, image annotation and retrieval tasks
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Visual graph modeling for scene recognition and mobile robot localization
Multimedia Tools and Applications
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We study in this paper the combination of different concept detection methods for conceptual indexing. Conceptual indexing shows effective results when large knowledge bases are available. But concept detection is not always accurate and errors limit interest of concept usage. A solution to solve this problem is to combine different concept detection methods. In this paper, we investigate several ways to combine concept detection methods, both on queries and documents, within the framework of the language modeling approach to IR. Our experiments show that our model fusion improves the standard language model by up to 17% on mean average precision.