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Automated Claim Detection in Argumentative Essays and their Relationship with Writing Quality

EasyChair Preprint 3850

10 pagesDate: July 13, 2020

Abstract

This study extracted content and structural features to predict human annotations for claims and non-claims in argumentative essays. The evaluation of classification models indicated Gaussian Naive Bayes classifier yielded the most balanced identifications of claims and non-claims. We used the model to make predictions in a validation corpus that included human ratings of writing quality. The number of claims, the percentage of non-claims, and the average position of non-claims were significant indicators of essay quality.

Keyphrases: argument mining, claim detection, essay quality

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:3850,
  author    = {Qian Wan and Scott Crossley and Laura Allen and Danielle McNamara},
  title     = {Automated Claim Detection in Argumentative Essays and their Relationship with Writing Quality},
  howpublished = {EasyChair Preprint 3850},
  year      = {EasyChair, 2020}}
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