To Normalize, or Not to Normalize:The Impact of Normalization on Part-of-Speech Tagging
Table 3: Effect of different models on canonical/non-canonical words.
['[EMPTY]', 'Bilty', '+Norm', '+Vecs', '+Comb']
Word embeddings have a higher impact on standard, canonical tokens. It is interesting to note that word embeddings and normalization both have a similar yet complementary effect on the words to be normalized (non-canonical). The improvements on non-canonical words seem to be complementary. The combined model additionally improves on words which need normalization, whereas it scores almost 1% lower on canonical words. This suggests that both strategies have potential to complement each other. Embeddings work considerably better than normalization, which confirms what we found on the Dev data. The combined approach yields the highest accuracy over all evaluation sets, however, it significantly differs from embeddings only on Test_L. This can be explained by our earlier observation (cf. Notice that Test_L does indeed contain the highest proportion of non-canonical tokens.