Software and hardware requirements

• python 2.7
• numpy
• Tensorflow 1.5+
• For fast training, a Nvidia graphic card or GPU

Credits

This code is based on the paper: https://arxiv.org/abs/1805.08237

Bernd Bohnet, Ryan McDonald, Gonçalo Simões, Daniel Andor, Emily Pitler, Joshua Maynez. Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings. ACL, 2018.

Our tagger ranked 1st for morphological features in the CoNLL-2018 Shared Task and had strong results for many languages on upos tags. The tagger is especially strong for cases where a wider context is required to determine the correct tag as for xpos and morphological features tagging.

Contributions: Bernd Bohnet, Ryan McDonald, Gonçalo Simões, Daniel Andor, Emily Pitler, Joshua Maynez, Terry Koo.

Training a tagger

python train_cw.py --train='en-wsj-std-train-stanford-3.3.0.conll'
--dev='en-wsj-std-dev-stanford-3.3.0.conll'
--embeddings='glove.6B.100d.txt'
--task='xtag'
--config='config.json'

The paths need to be adapted. The 'config.json' file contains the settings for the hyperparamerters. The settings for the number of LSTM layers, cells, etc. are smaller than the sizes used in the paper.

The input and output files are in CoNLL-U format: http://universaldependencies.org/format.html

The tagger supports three tasks: --task='upos' | 'xtag' | 'feats'

Applying a tagger

python test_cw.py --test='en-wsj-std-test-stanford-3.3.0.conll'
--task='xtag'
--output_dir='model_save_dir'
--out='output.conll'