Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
Gustavo Aguilar, Fahad AlGhamdi, Víctor Soto, Mona Diab, Julia Hirschberg, Thamar Solorio · 2018
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.