INK-USC / TriggerNER

TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition (ACL 2020)

TriggerNER

Code & Data for ACL 2020 paper:

TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition

Authors: Bill Yuchen Lin*, Dong-Ho Lee*, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, Xiang Ren

We introduce entity triggers, an effective proxy of human explanations for facilitating label-efficient learning of NER models. We crowd-sourced 14k entity triggers for two well-studied NER datasets. Our proposed model, name Trigger Matching Network, jointly learns trigger representations and soft matching module with self-attention such that can generalize to unseen sentences easily for tagging. Expriments show that the framework is significantly more cost-effective such that usinng 20% of the trigger-annotated sentences can result in a comparable performance of conventional supervised approaches using 70% training data.

If you make use of this code or the entity triggers in your work, please kindly cite the following paper:

@inproceedings{TriggerNER2020,
  title={TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition},
  author={Bill Yuchen Lin and Dong-Ho Lee and Ming Shen and Ryan Moreno and Xiao Huang  and Prashant Shiralkar and Xiang Ren}, 
  booktitle={Proceedings of ACL},
  year={2020}
}

Quick Links

• Requirements
• Trigger Dataset
• Train and Test

Trigger Dataset

The concept of entity triggers, a novel form of explanatory annotation for named entity recognition problems.
We crowd-source and publicly release 14k annotated entity triggers on two popular datasets: CoNLL03 (generic domain), BC5CDR (biomedical domain).

dataset/ saves CONLL, BC5CDR, and Laptop-Reviews dataset. For each directory,

• train.txt, test.txt, dev.txt are original dataset
• train_20.txt is for cutting out the original train dataset into 20% for baseline setting. The dataset is used in naive.py
• trigger_20.txt is trigger dataset. The dataset is used in supervised.py and semi_supervised.py.

To enable 3% of original training dataset, you should use --percentage 15 since the dataset we used for supervised.py and semi_supervised.py is 20% of original training data with triggers.

Requirements

Python >= 3.6 and PyTorch >= 0.4.1

python -m pip install -r requirements.txt

Train and Test

• Train/Test Baseline (Bi-LSTM / CRF with 20 % of training dataset) :

python naive.py --dataset CONLL
python naive.py --dataset BC5CDR

• Train/Test Trigger Matching Network in supervised setting :

python supervised.py --dataset CONLL
python supervised.py --dataset BC5CDR

• Train/Test Trigger Matching Network in semi-supervised setting (self-training) :

python semi_supervised.py --dataset CONLL
python semi_supervised.py --dataset BC5CDR

Our code is based on https://github.com/allanj/pytorch_lstmcrf.

INK Lab at USC