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A Visual Analysis of the Effects of Assumptions of Classical Probabilistic Models

Published:29 September 2013Publication History

ABSTRACT

This poster discusses the main assumptions of classical probabilistic models in IR by means of a visual data analysis approach. Starting from the problem of classification of documents into relevant and non relevant classes, we derive the exact same formula of the relevance weight of the Binary Independence Model but with more degrees of interaction. With this approach, new factors can be taken into account to obtain a different ranking of the documents.

References

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  1. A Visual Analysis of the Effects of Assumptions of Classical Probabilistic Models

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    • Published in

      cover image ACM Other conferences
      ICTIR '13: Proceedings of the 2013 Conference on the Theory of Information Retrieval
      September 2013
      148 pages
      ISBN:9781450321075
      DOI:10.1145/2499178

      Copyright © 2013 Copyright is held by the owner/author(s)

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 29 September 2013

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      Qualifiers

      • poster
      • Research
      • Refereed limited

      Acceptance Rates

      ICTIR '13 Paper Acceptance Rate11of51submissions,22%Overall Acceptance Rate209of482submissions,43%

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