LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs (D18-1)
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| Challenge: | We compare morphologically rich languages with analytical languages like English due to the large vocabulary size and data sparsity. |
| Approach: | They propose a featureless neural network architecture that generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models in Czech, German, and Arabic. |
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