Searching in Medline: Query expansion and manual indexing evaluation

  title={Searching in Medline: Query expansion and manual indexing evaluation},
  author={Samir Abdou and Jacques Savoy},
  journal={Inf. Process. Manage.},
Based on a relatively large subset representing one third of the MEDLINE collection, this paper evaluates ten different IR models, including recent developments in both probabilistic and language models. We show that the best performing IR models is a probabilistic model developed within the Divergence from Randomness framework [Amati, G., & van Rijsbergen, C.J. (2002) Probabilistic models of information retrieval based on measuring the divergence from randomness. ACMTransactions on Information… CONTINUE READING
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  • ACMTransactions on Information Systems 20(4), 357–389], which result in 170% enhancements in mean average precision when compared to the classical tf idf vector-space model.


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