A Multimodal Approach to the Medical Retrieval Task using IR-n.
Sergio Muñoz, Rafael Muñoz, Fernando Llopis · 2008
In our participation in the Medical Retrieval task we wanted to figure out if good results can be achieved with IR-n- our IR passage based system- for this restricted domain. We have focused on comparing the behaviour of two relevance feedback methods in this task- LCA and PRF-. Furthermore, in order to adapt our system to this task we have used two automatic query expansion techniques related with the medical domain. On one hand we have added to our system an automatic query expansion method based on MeSH ontology and on the other hand we have added a negative query expansion based on the acquisition type of the image. Finally we have added a multimodal re-ranking module- late fusion-. We have used two operation modes, one merges the two list in a classical re-ranking way, and the other mode bases the calculus of the relevance of an image on the quantity and the quality of the text related to the image in order to take the decision as to which system is more confident for that image- the system based on text or the one based on images-. A major finding of the results is that our passage based system fits very well to this task. Within the textual runs submitted by all the participants we have reached the 6th place for our baseline and the 1st place for a run using PRF and query expansion adapted to the medical domain. Our results for multimodal re-ranking have not been successful due to problems with the parameters tuning for the test collection of this year.