@InProceedings{levy-EtAl:2018:C18-1,
  author    = {Levy, Ran  and  Bogin, Ben  and  Gretz, Shai  and  Aharonov, Ranit  and  Slonim, Noam},
  title     = {Towards an argumentative content search engine using weak supervision},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {2066--2081},
  abstract  = {Searching for sentences containing claims in a large text corpus is a key component in developing an argumentative content search engine. Previous works focused on detecting claims in a small set of documents or within documents enriched with argumentative content. However, pinpointing relevant claims in massive unstructured corpora, received little attention. A step in this direction was taken in (Levy et al. 2017), where the authors suggested using a weak signal to develop a relatively strict query for claim--sentence detection. Here, we leverage this work to define weak signals for training DNNs to obtain significantly greater performance. This approach allows to relax the query and increase the potential coverage. },
  url       = {http://www.aclweb.org/anthology/C18-1176}
}