Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging
Kata Research Team Kata.ai（卡塔人工智能公司卡塔研究团队）；School of Informatics University of Edinburgh（爱丁堡大学信息学院）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
