@InProceedings{rubino-EtAl:2016:COLING,
  author    = {Rubino, Raphael  and  Degaetano-Ortlieb, Stefania  and  Teich, Elke  and  van Genabith, Josef},
  title     = {Modeling Diachronic Change in Scientific Writing with Information Density},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {750--761},
  abstract  = {Previous linguistic research on scientific writing has shown that language use
	in the scientific domain varies considerably in register and style over time.
	In this paper we investigate the introduction of information theory inspired
	features to study long term diachronic change on three levels: lexis,
	part-of-speech and syntax. Our approach is based on distinguishing between
	sentences from 19th and 20th century scientific abstracts using supervised
	classification models. To the best of our knowledge, the introduction of
	information theoretic features to this task is novel. We show that these
	features outperform more traditional features, such as token or character
	n-grams, while leading to more compact models. We present a detailed analysis
	of feature informativeness in order to gain a better understanding of
	diachronic change on different linguistic levels.},
  url       = {http://aclweb.org/anthology/C16-1072}
}