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tyx_neu f20b438392 | 1 year ago | |
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.. | ||
data | 1 year ago | |
README.md | 1 year ago | |
const.py | 1 year ago | |
corpus.py | 1 year ago | |
data_loader.py | 1 year ago | |
example.py | 1 year ago | |
model.py | 1 year ago | |
train.py | 1 year ago |
Module implemention from "Deep Semantic Role Labeling: What Works and What’s Next".
Get Tutorial if know Chinese
Perform experiments on the English data from CoNLL 2003
Have downloaded in ${PROJECT}/data
data
├── testa
├── testb
└── train
Step.1 - Generate Corpus.pt
python3 corpus.py
Step.2 - Train data
python3 train.py
label
{'<pad>': 0, 'B-MISC': 1, 'B-ORG': 2, 'I-MISC': 3, 'I-LOC': 4, 'I-PER': 5, 'B-LOC': 6, 'O': 7, 'I-ORG': 8}
result.1
sample: neuchatel @ st gallen @
predict: I-ORG O I-ORG I-ORG O
groud truth: I-ORG O I-ORG I-ORG O
result.2
sample: kankkunen has set an astonishing pace for a driver who has not rallied for three months .
predict: I-PER O O O I-PER O O O O O O O O O O O O
groud truth: I-PER O O O O O O O O O O O O O O O O
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