Taking Benefit of Query and Document Expansion using MeSH Descriptors in Medical ImageCLEF 2009.
Julien Gobeill, Douglas Theodoro, Emilie Pasche, Patrick Ruch · CLEF (Working Notes) · 2009
In 2008, we participated in medical ImageCLEF in order to compare different strategies of query and document expansion. Since we are a group specialized in Natural Language Processing, we discarded the visual aspect and we only dealt with the textual fields of the documents. We chose descriptors belonging to the Medical Subject Headings (MeSH) in order to expand the queries and the documents; therefore, these metadata were supposed to improve the retrieval process, and to be an interlingua between the collection and the topics for the multilingual tasks. MeSH descriptors for query and document expansion could be automatically computed via two strategies. Each document of this collection is provided with several fields describing the image such as title or caption; so we applied a local lexical MeSH categorizer to these fields in order to automatically extract a set of MeSH descriptors. Moreover, as each document is linked to a journal article via a PMID, we harvested the MeSH descriptors assigned to this article in MEDLINE in order to obtain a second set of MeSH descriptors. For the 2008 official runs, we chose to compare both strategies, but we subsequently showed in an unofficial run that combining them, by merging both sets of MeSH descriptors, led to the best performances. Therefore, combining both strategies increased the Mean Average Precision (MAP) of our best English official run from 0.176 in 2008 to 0.321 in 2009. Results for German and French runs were respectively MAP 0.231 and MAP 0.295.