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Text Segmentation with Topic Modeling and Entity Coherence

  • Adebayo Kolawole JohnEmail author
  • Luigi Di Caro
  • Guido Boella
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 552)

Abstract

This paper describes a system which uses entity and topic coherence for improved Text Segmentation (TS) accuracy. First, Linear Dirichlet Allocation (LDA) algorithm was used to obtain topics for sentences in the document. We then performed entity mapping across a window in order to discover the transition of entities within sentences. We used the information obtained to support our LDA-based boundary detection for proper boundary adjustment. We report the significance of the entity coherence approach as well as the superiority of our algorithm over existing works.

Keywords

Text segmentation Entity coherence Linear dirichlet allocation Topic modeling 

Notes

Acknowledgments

Kolawole J. Adebayo has received funding from the Erasmus Mundus Joint International Doctoral (Ph.D.) programme in Law, Science and Technology. Luigi Di Caro and Guido Boella have received funding from the European Union’s H2020 research and innovation programme under the grant agreement No 690974 for the project “MIREL: MIning and REasoning with Legal texts”.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Adebayo Kolawole John
    • 1
    Email author
  • Luigi Di Caro
    • 1
  • Guido Boella
    • 1
  1. 1.Dipartimento di InformaticaUniversita Di TorinoTorinoItaly

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