Applying Grapheme, Word, and Syllable Information for Language Identification in Code Switching Sentences

In this paper, we propose an automatic language identification approach for code switching sentences by using the morphological structures and sequence of the syllable. The approach was tested on Malay-English code switching sentences. The proposed language identification approach achieves 90.75% in term of accuracy on the vocabularies. Our approach was further improved by combining the knowledge from other level in the sentence: word and alphabet. The additional information further improves the accuracy of our language identification method to 96.36%.