Different Rankers on Different Subcollections
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  • 作者:Timothy Jones (19)
    Falk Scholer (19)
    Andrew Turpin (20)
    Stefano Mizzaro (21)
    Mark Sanderson (19)

    19. RMIT University
    ; Australia
    20. University of Melbourne
    ; Australia
    21. University of Udine
    ; Italy
  • 关键词:Collection Partitioning ; Subcollections ; Retrieval effectiveness
  • 刊名:Lecture Notes in Computer Science
  • 出版年:2015
  • 出版时间:2015
  • 年:2015
  • 卷:9022
  • 期:1
  • 页码:203-208
  • 全文大小:155 KB
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    7. Sanderson, M., Turpin, A., Zhang, Y., Scholer, F.: Differences in effectiveness across sub-collections. In: CIKM 2012, pp. 1965鈥?969. ACM (2012)
    8. Sheldon, D., Shokouhi, M., Szummer, M., Craswell, N. (2011) Lambdamerge: Merging the results of query reformulations. WSDM 2011. ACM, New York, pp. 795-804
    9. Thomas, P., Shokouhi, M. Evaluating server selection for federated search. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., R眉ger, S., Rijsbergen, K. eds. (2010) Advances in Information Retrieval. Springer, Heidelberg, pp. 607-610 CrossRef
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    11. Wu, M., Hawking, D., Turpin, A., Scholer, F. (2012) Using anchor text for homepage and topic distillation search tasks. JASIST 63: pp. 1235-1255 CrossRef
  • 作者单位:Advances in Information Retrieval
  • 丛书名:978-3-319-16353-6
  • 刊物类别:Computer Science
  • 刊物主题:Artificial Intelligence and Robotics
    Computer Communication Networks
    Software Engineering
    Data Encryption
    Database Management
    Computation by Abstract Devices
    Algorithm Analysis and Problem Complexity
  • 出版者:Springer Berlin / Heidelberg
  • ISSN:1611-3349
文摘
Recent work has shown that when documents in a TREC ad hoc collection are partitioned, different rankers will perform optimally on different partitions. This result suggests that choosing different highly effective rankers for each partition and merging the results, should be able to improve overall effectiveness. Analyzing results from a novel oracle merge process, we demonstrate that this is not the case: selecting the best performing ranker on each subcollection is very unlikely to outperform just using a single best ranker across the whole collection.

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