@InProceedings{aguilar-EtAl:2018:W18-32,
  author    = {Aguilar, Gustavo  and  AlGhamdi, Fahad  and  Soto, Victor  and  Diab, Mona  and  Hirschberg, Julia  and  Solorio, Thamar},
  title     = {Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task},
  booktitle = {Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {138--147},
  abstract  = {In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76\% and 71.61\% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.},
  url       = {http://www.aclweb.org/anthology/W18-3219}
}