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A Full-Text Learning to Rank Dataset for Medical Information Retrieval
[chapter]
2016
Lecture Notes in Computer Science
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 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
doi:10.1007/978-3-319-30671-1_58
fatcat:oftuozvprbdxzch4fvgvrf3oja