@InProceedings{mrabet-kilicoglu-demnerfushman:2017:Long,
  author    = {Mrabet, Yassine  and  Kilicoglu, Halil  and  Demner-Fushman, Dina},
  title     = {TextFlow: A Text Similarity Measure based on Continuous Sequences},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {763--772},
  abstract  = {Text similarity measures are used in multiple tasks such as plagiarism
	detection, information ranking and recognition of paraphrases and textual
	entailment. While recent advances in deep learning highlighted the relevance of
	sequential models in natural language generation, existing similarity measures
	do not fully exploit the sequential nature of language. Examples of such
	similarity measures include n-grams and skip-grams overlap which rely on
	distinct slices of the input texts. In this paper we present a novel text
	similarity measure inspired from a common representation in DNA sequence
	alignment algorithms. The new measure, called TextFlow, represents input text
	pairs as continuous curves and uses both the actual position of the words and
	sequence matching to compute the similarity value. Our experiments on 8
	different datasets show very encouraging results in paraphrase detection,
	textual entailment recognition and ranking relevance.},
  url       = {http://aclweb.org/anthology/P17-1071}
}