José Luis Martínez-Fernández

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Newspapers are one of the most challenging domains for information retrieval systems: new articles appear everyday written in different languages, with multimedia contents and the news repositories may be updated in a matter of hours so information extraction is crucial to the metadata contents of the news. Further approaches of “smart retrieval” have to(More)
One of the proposed tasks of the ImageCLEF 2005 campaign has been an Automatic Annotation Task. The objective is to provide the classification of a given set of 1,000 previously unseen medical (radiological) images according to 57 predefined categories covering different medical pathologies. 9,000 classified training images are given which can be used in(More)
The main goal of the bilingual and monolingual participation of the MIRACLE team in CLEF 2004 was to test the effect of combination approaches on information retrieval. The starting point was a set of basic components: stemming, transformation, filtering, generation of n-grams, weighting and relevance feedback. Some of these basic components were used in(More)
This paper presents the image retrieval techniques tested by the MIRACLE (Multilingual Information RetrievAl for the CLEf campaign) research group as part of the ImageCLEF 2004 initiative. Two main lines of research continuing the past year's experiments were considered: the application of linguistic techniques to improve retrieval performance and the(More)
This paper describes the first set of experiments defined by the MIRACLE (Multilingual Information RetrievAl for the CLEf campaign) research group for some of the cross language tasks defined by CLEF. These experiments combine different basic techniques, linguistic-oriented and statistic-oriented, to be applied to the indexing and retrieval processes.
We present the miraQA system that constitutes MIRACLE first experience in Question Answering for monolingual Spanish and has been developed for QA@CLEF 2004. The architecture of the system is described and details of our approach to Statistical Answer Extraction based on Hidden Markov Models are presented. One run that uses last year question set for(More)