Identification of Code-Switched Sentences and Words Using Language Modeling Approaches

Globalization and multilingualism contribute to code-switching—the phenomenon in which speakers produce utterances containing words or expressions from a second language. Processing code-switched sentences is a significant challenge for multilingual intelligent systems. This study proposes a language modeling approach to the problem of code-switching language processing, dividing the problem into two subtasks: the detection of code-switched sentences and the identification of code-switched words in sentences. A code-switched sentence is detected on the basis of whether it contains words or phrases from another language. Once the code-switched sentences are identified, the positions of the code-switched words in the sentences are then identified. Experimental results show that the language modeling approach achieved an F-measure of 80.43% and an accuracy of 79.01% for detecting Mandarin-Taiwanese code-switched sentences. For the identification of code-switched words, the word-based and POS-based models, respectively, achieved F-measures of 41.09% and 53.08%