Analysis combination and pseudo relevance feedback in conceptual language model: LIRIS participation at ImageCLEFMed

Abstract : 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.
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Loïc Maisonnasse, Farah Harrathi, Catherine Roussey, Sylvie Calabretto. Analysis combination and pseudo relevance feedback in conceptual language model: LIRIS participation at ImageCLEFMed. Lecture Notes in Computer Science, Springer, 2010, 6242, p. 203 - p. 210. ⟨10.1007/978-3-642-15751-6⟩. ⟨hal-00527113⟩

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