LinCE - MSA-EGY (NER - Code Switching)
Modern Standard ArabicDialectal Arabic (MSA-DA)
Multiple institutions 
Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
named entity recognition
Workshop on Computational Approaches to Code Switching
Gustavo Aguilar,F. Alghamdi,Víctor Soto,Mona T. Diab,Julia Hirschberg,T. Solorio
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.