Year: 2014
Type: conference-paper
Abstract: 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.
DOI: 10.3115/v1/w14-3911
Authors: Heba Elfardy, Mohamed Al-Badrashiny, Mona Diab
Institutions: Columbia University, George Washington University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2251530528