St a n z a : A Python Natural Language Processing Toolkitfor Many Human Languages
Table 2: Neural pipeline performance comparisons on the Universal Dependencies (v2.5) test treebanks. For our system we show macro-averaged results over all 100 treebanks. We also compare our system against UDPipe and spaCy on treebanks of five major languages where the corresponding pretrained models are publicly available. All results are F1 scores produced by the 2018 UD Shared Task official evaluation script.
['Treebank', 'System', 'Tokens', 'Sents.', 'Words', 'UPOS', 'XPOS', 'UFeats', 'Lemmas', 'UAS', 'LAS']
For performance on UD treebanks, we compared St a n z a (v1.0) against UDPipe (v1.2) and spaCy (v2.2) on treebanks of 5 major languages whenever a pretrained model is available. a n z a achieved the best performance on most scores reported. Notably, we find that St a n z a ’s language-agnostic architecture is able to adapt to datasets of different languages and genres. This is also shown by St a n z a ’s high macro-averaged scores over 100 treebanks covering 66 languages.