@InProceedings{csulea:2017:RepEval,
  author    = {\c{S}ulea, Octavia-Maria},
  title     = {Recognizing Textual Entailment in Twitter Using Word Embeddings},
  booktitle = {Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {31--35},
  abstract  = {In this paper, we investigate the application of machine learning techniques
	and word embeddings to the task of Recognizing Textual Entailment (RTE) in
	Social Media. We look at a manually labeled dataset consisting of user
	generated short texts posted on Twitter (tweets) and related to four recent
	media events (the Charlie Hebdo shooting, the Ottawa shooting, the Sydney
	Siege, and the German Wings crash) and test to what extent neural techniques
	and embeddings are able to distinguish between tweets that entail or contradict
	each other or that claim unrelated things. We obtain comparable results to the
	state of the art in a train-test setting, but we show that, due to the noisy
	aspect of the data, results plummet in an evaluation strategy crafted to better
	simulate a real-life train-test scenario.},
  url       = {http://www.aclweb.org/anthology/W17-5306}
}