A Morphology-aware Network for Morphological Disambiguation
Table 7: The comparison of the disambiguation accuracy of the proposed approach with the state-of-the-art models in Turkish.
['Method', 'Accuracy(%)']
We present Turkish morphological disambiguation results obtained by our model with and without pre-training in lines 3 and 4 respectively. As we discussed before, unsupervised pre-training of the embeddings can boost accuracies of neural networks. As expected, morphological disambiguation accuracy increases by around 1% (around 6% reduction in error) when root embeddings are pre-trained instead of randomly initialized. We see that even without unsupervised pre-training our algorithm outperforms the current state of the art models and we are able to further improve the accuracy by pre-training of the embeddings.