Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging
This work addresses the problem of inconsistent comparisons in Indonesian NLP research by providing a standardized dataset split, though it is incremental in applying established neural methods to a specific language.
The paper tackled the lack of standardized evaluation for Indonesian part-of-speech tagging by exploring various models and achieved a new state-of-the-art F1 score of 97.47 using a recurrent neural network on the IDN Tagged Corpus.
Previous work in Indonesian part-of-speech (POS) tagging are hard to compare as they are not evaluated on a common dataset. Furthermore, in spite of the success of neural network models for English POS tagging, they are rarely explored for Indonesian. In this paper, we explored various techniques for Indonesian POS tagging, including rule-based, CRF, and neural network-based models. We evaluated our models on the IDN Tagged Corpus. A new state-of-the-art of 97.47 F1 score is achieved with a recurrent neural network. To provide a standard for future work, we release the dataset split that we used publicly.