Stylometric analysis of scientific articles

  • Authors:
  • Shane Bergsma;Matt Post;David Yarowsky

  • Affiliations:
  • Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD

  • Venue:
  • NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Year:
  • 2012

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Abstract

We present an approach to automatically recover hidden attributes of scientific articles, such as whether the author is a native English speaker, whether the author is a male or a female, and whether the paper was published in a conference or workshop proceedings. We train classifiers to predict these attributes in computational linguistics papers. The classifiers perform well in this challenging domain, identifying non-native writing with 95% accuracy (over a baseline of 67%). We show the benefits of using syntactic features in stylometry; syntax leads to significant improvements over bag-of-words models on all three tasks, achieving 10% to 25% relative error reduction. We give a detailed analysis of which words and syntax most predict a particular attribute, and we show a strong correlation between our predictions and a paper's number of citations.