@InProceedings{song-EtAl:2016:COLING1,
  author    = {Song, Wei  and  Liu, Tong  and  Fu, Ruiji  and  Liu, Lizhen  and  Wang, Hanshi  and  Liu, Ting},
  title     = {Learning to Identify Sentence Parallelism in Student Essays},
  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     = {794--803},
  abstract  = {Parallelism is an important rhetorical device. We propose a machine learning
	approach for automated sentence parallelism identification in student essays.
	We build an essay dataset with sentence level parallelism annotated. We derive
	features by combining generalized word alignment strategies and the alignment
	measures between word sequences. The experimental results show that sentence
	parallelism can be effectively identified with a F1 score of 82% at pair-wise
	level and 72% at parallelism chunk level.Based on this approach, we
	automatically identify sentence parallelism in more than 2000 student essays
	and study the correlation between the use of sentence parallelism and the types
	and quality of essays.},
  url       = {http://aclweb.org/anthology/C16-1076}
}