NFETC
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billy-inn
Neural Fine-grained Entity Type Classification https://arxiv.org/abs/1803.03378
Hi, After downloading the corpus and preprocessing using `transform.py`, we find that 92.9% of wikim test samples have one-hot labels. The statistics are different from those shown in the paper....
for example ，I got 88 types in Ontonote，not 89. {'/other/event': 0, '/other/food': 1, '/other/living_thing/animal': 2, '/other/internet': 3, '/organization/government': 4, '/location/structure/airport': 5, '/other/body_part': 6, '/location/transit/railway': 7, '/organization/transit': 8, '/other/legal': 9, '/organization/company':...
