Finding Function in Form: Compositional Character Models forOpen Vocabulary Word Representation
Table 4: POS accuracies on different languages
['System', 'Fusional EN', 'Fusional PT', 'Fusional CA', 'Agglutinative DE', 'Agglutinative TR']
From these results, we can see that in general, it is clear that C2W always outperforms word lookup tables (row “Word”), and that improvements are especially pronounced in Turkish, which is a highly morphological language, where word meanings differ radically depending on the suffixes used (evde → in the house vs. evden → from the house). In general, we can observe that the model using word lookup tables (row “Word”) performs consistently worse than the C2W model (row “C2W”). Results using these tagger are comparable or better than state-of-the-art systems. We can observe that in most cases we can slightly outperform the scores obtained using their tagger. This is a promising result, considering that we use the same training data and do not handcraft any features. Furthermore, we can observe that for Turkish, our results are significantly higher (>4%).