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POS tagging of Chinese Buddhist texts using Recurrent Neural Networks

Chinese POS tagging, as one of the most important problems in the NLP community, has been investigated in the past decades. This project, for the first time in the literature, tests different neural network models on a Chinese Buddhist contexts, which are the representative for the Medieval Chinese. Our results demonstrate the capacity of neural network models, and the results are than the popular trigram HMM model in the literature. Differences between the Buddhist texts and modern Chinese data are also revealed by the experiments. Lastly, we also propose several interesting topics for future research.