End to End Korean Part of Speech Tagging Using Copying Mechanism
In this article, we introduce a novel neural architecture for the end to end Korean Part of Speech (POS) tagging problem. To address the problem, we extend the present recurrent neural network based sequence to sequence models to deal with the key challenges in this task: rare word generation and POS tagging. To overcome these issues, Input Feeding and Copying mechanism are adopted. Although our approach does not require any manual features or preprocessed pattern matching dictionaries, our best single model achieves an F score of 97.08. This is competitive with the current state of the art model (F score 98.03) which requires extensive manual feature processing.
ACM Trans. Asian Low Resour. Lang. Inf. Process.