In this paper, we present the latest version of our system for identifying linguistic code switching in Arabic text. The system relies on Language Models and a tool for morphological analysis and disambiguation for Arabic to identify the class of each word in a given sentence. We evaluate the performance of our system on the test datasets of the shared task at the EMNLP workshop on Computational Approaches to Code Switching (Solorio et al., 2014) . The system yields an average token-level F β=1 score of 93.6%, 77.7% and 80.1%, on the first, second, and surprise-genre test-sets, respectively, and a tweet-level F β=1 score of 4.4%, 36% and 27.7%, on the same test-sets.
AIDA REF ) uses a weakly supervised rule based approach that relies on a language model to tag each word in the given sentence.
AIDA: Identifying Code Switching in Informal Arabic Text
CodeSwitch@EMNLP
Computer Science