Neural Morphological Tagging from Characters for Morphologically Rich Languages

Understand

This paper investigates neural character-based morphological tagging for languages with complex morphology and large tag sets.

• We systematically explore a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain character-based word vectors combined with bidirectional LSTMs to model across-word context in an end-to-end setting.
• We explore supplementary use of word-based vectors trained on large amounts of unlabeled data.
• Our experiments for morphological tagging suggest that for "simple" model configurations, the choice of the network architecture (CNN vs.