OHSUMED

OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research. A series of information retrieval experiments was carried out with a computer installed in a medical practice setting for relatively inexperienced physician end-users. Using a commercial MEDLINE product based on the vector space model, these physicians searched just as effectively as more experienced searchers using Boolean searching. The results of this experiment were subsequently used to create a new large medical test collection, which was used in experiments with the SMART retrieval system to obtain baseline performance data as well as compare SMART with the other searchers.


References in zbMATH (referenced in 32 articles )

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  1. Werner, Tino: A review on instance ranking problems in statistical learning (2022)
  2. Moreo, Alejandro; Esuli, Andrea; Sebastiani, Fabrizio: Word-class embeddings for multiclass text classification (2021)
  3. France, Stephen L.; Chen, Wen; Deng, Yumin: ADCLUS and INDCLUS: analysis, experimentation, and meta-heuristic algorithm extensions (2017)
  4. Jiang, Chunheng; Lin, Wenbin: DEARank: a data-envelopment-analysis-based ranking method (2015)
  5. Veningston, K.; Shanmugalakshmi, R.; Nirmala, V.: Semantic association ranking schemes for information retrieval applications using term association graph representation (2015)
  6. Cao, Jie; Wu, Zhiang; Wu, Junjie: Scaling up cosine interesting pattern discovery: a depth-first method (2014) ioport
  7. Jung, Kyu-Hwan; Lee, Jaewook: Probabilistic generative ranking method based on multi-support vector domain description (2013)
  8. Pillai, Ignazio; Fumera, Giorgio; Roli, Fabio: Multi-label classification with a reject option (2013) ioport
  9. France, Stephen L.; Carroll, J. Douglas; Xiong, Hui: Distance metrics for high dimensional nearest neighborhood recovery: compression and normalization (2012) ioport
  10. Kurt Hornik; Ingo Feinerer; Martin Kober; Christian Buchta: Spherical k-Means Clustering (2012) not zbMATH
  11. Policicchio, Veronica L.; Pietramala, Adriana; Rullo, Pasquale: GAMoN: discovering (M)-of-(N^\neg, \lor) hypotheses for text classification by a lattice-based genetic algorithm (2012)
  12. Rubin, Timothy N.; Chambers, America; Smyth, Padhraic; Steyvers, Mark: Statistical topic models for multi-label document classification (2012)
  13. D’hondt, Joris; Verhaegen, Paul-Armand; Vertommen, Joris; Cattrysse, Dirk; Duflou, Joost R.: Topic identification based on document coherence and spectral analysis (2011) ioport
  14. Yu, Hwanjo: Selective sampling techniques for feedback-based data retrieval (2011)
  15. D’Hondt, Joris; Vertommen, Joris; Verhaegen, Paul-Armand; Cattrysse, Dirk; Duflou, Joost R.: Pairwise-adaptive dissimilarity measure for document clustering (2010) ioport
  16. Yu, Hwanjo; Kim, Taehoon; Oh, Jinoh; Ko, Ilhwan; Kim, Sungchul; Han, Wook-Shin: Enabling multi-level relevance feedback on pubmed by integrating rank learning into DBMS (2010) ioport
  17. Aiolli, Fabio; Cardin, Riccardo; Sebastiani, Fabrizio; Sperduti, Alessandro: Preferential text classification: learning algorithms and evaluation measures (2009) ioport
  18. Stokes, Nicola; Li, Yi; Cavedon, Lawrence; Zobel, Justin: Exploring criteria for successful query expansion in the genomic domain (2009) ioport
  19. Wang, Pu; Hu, Jian; Zeng, Hua-Jun; Chen, Zheng: Using Wikipedia knowledge to improve text classification (2009) ioport
  20. Chen, Yanhua; Rege, Manjeet; Dong, Ming; Hua, Jing: Non-negative matrix factorization for semi-supervised data clustering (2008) ioport

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