A Full-Text Learning to Rank Dataset for Medical Information Retrieval

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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 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/.

Vera Boteva, Demian Gholipour, Artem Sokolov, Stef

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ECIR 2016 | Information Technology |

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Added	02 Apr 2016
Updated	02 Apr 2016
Type	Journal
Year	2016
Where	ECIR
Authors	Vera Boteva, Demian Gholipour, Artem Sokolov, Stefan Riezler

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A Full-Text Learning to Rank Dataset for Medical Information Retrieval

ECIR 2016 | Information Technology |

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