Lexical Normalization and POS-tagging for Code-switched Data
Table 5: Accuracies for POS tagging, using a variety of normalization strategies.
['[EMPTY]', 'LAI', 'Multilingual', 'Language-aware', 'Gold']
Comparing the different models, we see that MarMoT underperforms on all settings. A manual inspection revealed that this is mainly due to misclassification of emoticons (as PUNCT), which do not occur in the training data. In this setup, performance was generally higher, and the gains when using normalization were smaller.