Abstract
We present a dataset for learning to rank in the medical domain, consisting of thousands of full-text queries that are linked to thousands of research articles. The queries are taken from health topics described in layman’s English on the non-commercial www. NutritionFacts.org website; relevance links are extracted at 3 levels from direct and indirect links of queries to research articles on PubMed. We demonstrate that ranking models trained on this dataset by far outperform standard bag-of-words retrieval models. The dataset can be downloaded from: www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/.
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CITATION STYLE
Boteva, V., Gholipour, D., Sokolov, A., & Riezler, S. (2016). A full-text learning to rank dataset for medical information retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9626, pp. 716–722). Springer Verlag. https://doi.org/10.1007/978-3-319-30671-1_58
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