%0 Conference Proceedings %T Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task %A Aguilar, Gustavo %A AlGhamdi, Fahad %A Soto, Victor %A Diab, Mona %A Hirschberg, Julia %A Solorio, Thamar %Y Aguilar, Gustavo %Y AlGhamdi, Fahad %Y Soto, Victor %Y Solorio, Thamar %Y Diab, Mona %Y Hirschberg, Julia %S Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching %D 2018 %8 July %I Association for Computational Linguistics %C Melbourne, Australia %F aguilar-etal-2018-named %X 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. %R 10.18653/v1/W18-3219 %U https://aclanthology.org/W18-3219 %U https://doi.org/10.18653/v1/W18-3219 %P 138-147