Adversarial NER

Code used in the paper Resilience of Named Entity Recognition Models Under Adversarial Attack.

Usage

Convert IOB/IOBES tagging scheme to IO

python iox-to-io.py data.conll to convert a CoNLL format file with IOB/IOBES tagging scheme into IO tagging scheme. Generates data-io.conll from data.conll.

Generate adversarial evaluation file

python adversarial.py data-io.conll to generate the CoNLL format adversarial evaluation files. Generates:

• data-io-ablation.conll for Case Ablation.
• data-io-aberration.conll for Case Aberration.
• data-io-perturbation.conll for Context Perturbation.
• data-io-alteration.conll for Context Alteration.

Datasets

1. CoNLL
2. Wiki
3. IEER
4. GMB

Citing

If you find this code useful, please cite Resilience of Named Entity Recognition Models Under Adversarial Attack.

@inproceedings{das-paik-2022-resilience,
title = {Resilience of Named Entity Recognition Models under Adversarial Attack},
author = {Das, Sudeshna and Paik, Jiaul H},
booktitle = {Proceedings of the First Workshop on Dynamic Adversarial Data Collection},
month = {jul},
year = {2022},
address = {Seattle, WA},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.dadc-1.1},
pages = {1--6},
}