  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.
