Word association norms, mutual information, and lexicography
Computational Linguistics
Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
Multiplying Concept Sources for Graph Modeling
Advances in Multilingual and Multimodal Information Retrieval
Model Fusion in Conceptual Language Modeling
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
The MedGIFT group at ImageCLEF 2009
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
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This paper presents the LIRIS contribution to the CLEF 2009 medical retrieval task (i.e. ImageCLEFmed). Our model makes use of the textual part of the corpus and of the medical knowledge found in the Unified Medical Language System (UMLS) knowledge sources. As proposed in [6] last year, we used a conceptual representation for each sentence and we proposed a language modeling approach. We test two versions of conceptual unigram language model; one that use the log-probability of the query and a second one that compute the Kullback-Leibler divergence. We used different concept detection methods and we combine these detection methods on queries and documents. This year we mainly test the impact of the use of additional analysis on queries. We also test combinations on French queries where we combine translation and analysis, in order to solve the lack of French terms in UMLS, this provide good results close from the English ones. To complete these combinations we proposed a pseudo relevance method. This approach use the n first retrieve documents to form one pseudo query that is used in the Kullback-Leibler model to complete the original query. The results of this approach show that extending the queries with such an approach improves the results.