Various neural networks for sequence labeling tasks have been studied extensively in recent years. The main research focus on neural networks for the task are range from the feed-forward neural network to the long short term memory (LSTM) network with CRF layer. This paper summarizes the existing neural architectures and develop the most representative four neural networks for part-of-speech tagging and apply them on several typologically different languages. Experimental results show that the LSTM type of networks outperforms the feed-forward network in most cases and the character-level networks can learn the lexical features from characters within words, which makes the model achieve better results than no-character ones.
CITATION STYLE
Toleu, A., Tolegen, G., & Mussabayev, R. (2020). Deep Learning for Multilingual POS Tagging. In Communications in Computer and Information Science (Vol. 1287, pp. 15–24). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63119-2_2
Mendeley helps you to discover research relevant for your work.