Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging
TABLE I: Dev F1 score of each neural tagger architecture
['[BOLD] Architecture', '[BOLD] Mean F1']
Firstly, we report on our tuning experiments for the neural tagger. We group the results by the encoding and prediction step configuration. For each group, we show the highest F1 score among many embedding configurations. As we can see, biLSTM with CRF layer achieves 97.60 F1 score, the best score on the development set. Therefore, we will use this tagger to represent the neural model hereinafter.