OpenNER

The code and data of Towards Open-Domain Named Entity Recognition via Neural Correction Models

Requirements

• python3
• pip3 install -r requirements.txt

Download

• Models:
  • OpenNER-base: model
  • OpenNER-large: model
• Data set
  • AnchorNER: data

You can download the models above and put them wherever you want. You only need to set the model_dir parameter in the OpenNER class to the address where the model is located. See the examples below.

Example Usage

1. Load OpenNER in python

Parameter setting

from OpenNER import OpenNER

# load OpenNER-base
tagger = OpenNER()

# load OpenNER-large
tagger = OpenNER(bert_model='bert-large-cased', model_dir='model/OpenNER_large')

The default value of bert_model is bert-base-cased and the default value of model_dir is 'model/OpenNER_base'.

If you want to load OpenNER-large, you need to change bert_model to bert-large-cased.

If the location of your model is not in the default address, please change model_dir to the address where your model is located.

Predict one sentence

from OpenNER import OpenNER

# load OpenNER
tagger = OpenNER()

# run NER over sentence
print(tagger.predict("Despite winning the Asian Games title two years ago, Uzbekistan are in the finals as outsiders."))  

This should print:

['Despite O', 'winning O', 'the O', 'Asian B-MISC', 'Games I-MISC', 'title O', 'two O', 'years O', 'ago O', ', O', 'Uzbekistan B-LOC', 'are O', 'in O', 'the O', 'finals O', 'as O', 'outsiders O', '. O']  

Predict multiple sentences

You can also predict a batch of sentences, using the following codes:

# run NER over a batch of sentences
print(tagger.predict_batch(["Despite winning the Asian Games title two years ago, Uzbekistan are in the finals as outsiders.", "William Wang is an Assistant Professor from UCSB."]))  

This should print:

[['Despite O', 'winning O', 'the O', 'Asian B-MISC', 'Games I-MISC', 'title O', 'two O', 'years O', 'ago O', ', O', 'Uzbekistan B-LOC', 'are O', 'in O', 'the O', 'finals O', 'as O', 'outsiders O', '. O'],   
['William B-PER', 'Wang I-PER', 'is O', 'an O', 'Assistant O', 'Professor O', 'from O', 'UCSB B-ORG', '. O']]  

Predict sentences in file

You can also predict sentences in file, using the following codes:

# run NER over a file containing multiple sentences
tagger.predict_file("input.txt", "output.txt")  

The format of input file should be one word per line and each sentence is separated by a blank line.

For example:

William
Wang
is
from
UCSB
.

Each line of the output file is in this format: "token tag"

For example:

William B-PER
Wang I-PER
is O
from O
UCSB B-ORG
. O

2. Use OpenNER in command

CUDA_VISIBLE_DEVICES=0 python main.py --input=input.txt --output=output.txt --bert_model=bert-base-cased --model_dir=model/OpenNER_base/ --max_seq_length=128 --eval_batch_size=32

3. Train correction model

Train

Each line of the input file is in this format: "token Wiki_label DocRED_label"

cd correction
python run_correction_model.py --train_file=data/D1_train.txt --dev_file=data/D1_dev.txt --test_file=data/D1_test.txt --bert_model=bert-base-cased --task_name=ner --output_dir=out_D1_model --max_seq_length=128 --do_train --num_train_epochs 5 --do_eval --warmup_proportion=0.4

Test

Remove --do_train and specify pred_file to save outputs

python run_correction_model.py --train_file=data/D1_train.txt --dev_file=data/D1_dev.txt --test_file=data/D1_test.txt --pred_file=data/pred.txt --bert_model=bert-base-cased --task_name=ner --output_dir=out_D1_model --max_seq_length=128 --do_eval --warmup_proportion=0.4