This study presents the development and evaluation of pattern matching refinements (PMRs) to automatic code switching point (CSP) detection. With all PMRs, evaluation showed an accuracy of 94.51%. This is an improvement to reported accuracy rates of dictionary-based approaches, which are in the range of 75.22%-76.26% (Yeong and Tan, 2010) . In our experiments, a 100-sentence Tagalog-English corpus was used as test bed. Analyses showed that the dictionary-based approach using part-ofspeech checking yielded an accuracy of 79.76% only, and two notable linguistic phenomena, (1) intra-word code-switching and (2) common words, were shown to have caused the low accuracy. The devised PMRs, namely: (1) common word exclusion, (2) common word identification, and (3) common n-gram pruning address this and showed improved accuracy. The work can be extended using audio files and machine learning with larger language resources.
REF focused on detecting code-switching points and noted that intra-word code-switching caused difficulties to a dictionary based approach.
Pattern Matching Refinements to Dictionary-Based Code-Switching Point Detection
PACLIC
Computer Science