In this paper, we report on the fusion of simple retrieval strategies with thesaural resources in order to perform document and query translation for cross{language retrieval in a collection of medical cases. The collection contains textual and visual contents. In this paper, we focus on the textual contents of the collection, which contains documents in three languages: French, English and German. The fusion of visual and textual content will also be treated. Unlike most automatic categorization systems, which rely on training data in order to infer text{to{concept relationships, our approach can be applied with any controlled vocabulary and does not use any training data. For the 2006 ImageCLEFmed experiments we use the Medical Subject Headings (MeSH), a terminology maintained by the National Library of Medicine and which exists in a dozen languages. The basic idea consists of annotating every textual content of the collection (documents and queries) with a set of MeSH concepts using an automatic text categoriser. Thus, allowing an interlingual mapping between queries and documents. For tuning purposes, the system uses a sample of MEDLINE from the OHSUMED collection. Our results, conrmed that such a simple approach is competitive with best performing cross-language retrieval methods for such a collection. Several simple linear approaches were used to combine textual and visual features
['Julien Gobeill', 'Henning Müller', 'Patrick Ruch']
Query and Document Translation by Automatic Text Categorization: A Simple Approach to Establish a Strong Textual Baseline for ImageCLEFmed 2006