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Document Performance Prediction for Automatic Text Classification

  • Gustavo Penha
  • Raphael Campos
  • Sérgio Canuto
  • Marcos André Gonçalves
  • Rodrygo L. T. SantosEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)

Abstract

Query performance prediction (QPP) is a fundamental task in information retrieval, which concerns predicting the effectiveness of a ranking model for a given query in the absence of relevance information. Despite being an active research area, this task has not yet been explored in the context of automatic text classification. In this paper, we study the task of predicting the effectiveness of a classifier for a given document, which we refer to as document performance prediction (DPP). Our experiments on several text classification datasets for both categorization and sentiment analysis attest the effectiveness and complementarity of several DPP inspired by related QPP approaches. Finally, we also explore the usefulness of DPP for improving the classification itself, by using them as additional features in a classification ensemble.

Keywords

Performance prediction Automatic text classification 

Notes

Acknowledgements

Work partially funded by project MASWeb (FAPEMIG APQ-01400-14) and by the authors’ individual grants from CNPq and FAPEMIG.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gustavo Penha
    • 1
  • Raphael Campos
    • 2
  • Sérgio Canuto
    • 2
  • Marcos André Gonçalves
    • 2
  • Rodrygo L. T. Santos
    • 2
    Email author
  1. 1.Delft University of TechnologyDelftThe Netherlands
  2. 2.Computer Science DepartmentUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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