Recursive style breach detection with multifaceted ensemble learning

Daniel Kopev, Dimitrina Zlatkova, Kristiyan Mitov, Atanas Atanasov, Momchil Hardalov, Ivan Koychev, Preslav Nakov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a supervised approach for style change detection, which aims at predicting whether there are changes in the style in a given text document, as well as at finding the exact positions where such changes occur. In particular, we combine a TF.IDF representation of the document with features specifically engineered for the task, and we make predictions via an ensemble of diverse classifiers including SVM, Random Forest, AdaBoost, MLP, and LightGBM. Whenever the model detects that style change is present, we apply it recursively, looking to find the specific positions of the change. Our approach powered the winning system for the PAN@CLEF 2018 task on Style Change Detection.

Original languageEnglish
Title of host publicationArtificial Intelligence
Subtitle of host publicationMethodology, Systems, and Applications - 18th International Conference, AIMSA 2018, Proceedings
EditorsJosef van Genabith, Gennady Agre, Thierry Declerck
PublisherSpringer Verlag
Pages126-137
Number of pages12
ISBN (Print)9783319993430
DOIs
Publication statusPublished - 1 Jan 2018
Event18th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2018 - Varna, Bulgaria
Duration: 12 Sep 201814 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11089 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2018
CountryBulgaria
CityVarna
Period12/9/1814/9/18

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Keywords

  • Gradient boosting machines
  • Multi-authorship
  • Natural language processing
  • Stacking ensemble
  • Style breach detection
  • Style change detection
  • Stylometry

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kopev, D., Zlatkova, D., Mitov, K., Atanasov, A., Hardalov, M., Koychev, I., & Nakov, P. (2018). Recursive style breach detection with multifaceted ensemble learning. In J. van Genabith, G. Agre, & T. Declerck (Eds.), Artificial Intelligence: Methodology, Systems, and Applications - 18th International Conference, AIMSA 2018, Proceedings (pp. 126-137). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11089 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-99344-7_12