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Mining Argument Components in Essays at Different Levels

EasyChair Preprint no. 10521

14 pagesDate: July 9, 2023


The research of arguments in student essays has long been the subject of automatic approaches to argument mining. The task has often been modeled as a sequence tagging problem where the text is ei- ther analyzed in its entirety by a transformer model or split into smaller homogeneous units, such as sentences or paragraphs. However, previous research has highlighted how the various text sections may have different functions, and how the position of specific argument components obeys precise structural dependency criteria. For this reason, we propose an approach exploiting such structural information: in this work we present a hybrid training approach that takes into account the specific structural part of the essays, in order to be able to mine different types of argu- ment components at different levels. Our hybrid approach achieved an improvement over essay-level and paragraph-level training, in particular in the extraction of some specific argument components.

Keyphrases: argument mining, machine learning, Natural Language Processing, transformers

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Roberto Demaria and Davide Colla and Matteo Delsanto and Enrico Mensa and Enrico Pasini and Daniele P. Radicioni},
  title = {Mining Argument Components in Essays at Different Levels},
  howpublished = {EasyChair Preprint no. 10521},

  year = {EasyChair, 2023}}
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