This is the implementation of "Named Entity Recognition using Positive-Unlabeled Learning" published at ACL2019.

###Set up and run Download glove.6B.100d.txt

pytorch 1.1.0 python 3.6.4 cuda 8.0

Train Print parameters run feature_pu_model.py --h optional arguments: -h, --help show this help message and exit --lr LR learning rate --beta BETA beta of pu learning (default 0.0) --gamma GAMMA gamma of pu learning (default 1.0) --drop_out DROP_OUT dropout rate --m M class balance rate --flag FLAG entity type (PER/LOC/ORG/MISC) --dataset DATASET name of the dataset --batch_size BATCH_SIZE batch size for training and testing --print_time PRINT_TIME epochs for printing result --pert PERT percentage of data use for training --type TYPE pu learning type (bnpu/bpu/upu) e.g.) Train on PER type of conll2003 dataset: python feature_pu_model.py --dataset conll2003 --type PER ** Evaluating** python feature_pu_model_evl.py --model saved_model/bnpu_conll2003_PER_lr_0.0001_prior_0.3_beta_0.0_gamma_1.0_percent_1.0 --flag PER --dataset conll2003 --output 1 replace the model name from the training python final_evl.py Get the final result on all the entity type. Remember to revise the filenames to be the output file name of evaluating.

dictionary generation run python ada_dict_generation.py -h optional arguments: -h, --help show this help message and exit --beta BETA learning rate --gamma GAMMA gamma of pu learning (default 1.0) --drop_out DROP_OUT dropout rate --m M class balance rate --flag FLAG entity type (PER/LOC/ORG/MISC) --dataset DATASET name of the dataset --lr LR learning rate --batch_size BATCH_SIZE batch size for training and testing --iter ITER iteration time --unlabeled UNLABELED use unlabeled data or not --pert PERT percentage of data use for training --model MODEL saved model name e.g.) python ada_dict_generation.py --model saved_model/bnpu_conll2003_PER_lr_0.0001_prior_0.3_beta_0.0_gamma_1.0_percent_1.0 --flag PER --iter 1 adaptive training run python adaptive_pu_model.py -h optional arguments: -h, --help show this help message and exit --beta BETA beta of pu learning (default 0.0) --gamma GAMMA gamma of pu learning (default 1.0) --drop_out DROP_OUT dropout rate --m M class balance rate --p P estimate value of prior --flag FLAG entity type (PER/LOC/ORG/MISC) --dataset DATASET name of the dataset --lr LR learning rate --batch_size BATCH_SIZE batch size for training and testing --output OUTPUT write the test result, set 1 for writing result to file --model MODEL saved model name --iter ITER iteration time e.g.) python adaptive_pu_model.py --model saved_model/bnpu_conll2003_PER_lr_0.0001_prior_0.3_beta_0.0_gamma_1.0_percent_1.0 --flag PER --iter 1` Replace saved model names and iteration times when doing adaptive learning. And in the same iteration the iter number in dictionary generation and adaptive learning should be same.