A Unified MRC Framework for Named Entity Recognition

The repository contains the code of the recent research advances in Shannon.AI.

A Unified MRC Framework for Named Entity Recognition
Xiaoya Li*, Jingrong Feng*, Yuxian Meng, Qinghong Han, Fei Wu, Jiwei Li
Preprint. arXiv
If you find this repo helpful, please cite the following:

@article{li2019unified,
  title={A Unified MRC Framework for Named Entity Recognition},
  author={Li, Xiaoya and Feng, Jingrong and Meng, Yuxian and Han, Qinghong and Wu, Fei and Li, Jiwei},
  journal={arXiv preprint arXiv:1910.11476},
  year={2019}
}

For any question, please feel free to contact [email protected] or post Github issue.

Contents

1. Overview
2. Experimental Results on Flat/Nested NER Datasets
3. Data Preparation
4. Dependencies
5. Usage
6. FAQ

Overview

The task of NER is normally divided into nested NER and flat NER depending on whether named entities are nested or not. Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a SQuAD-style machine reading comprehension (MRC) task.

For example, the task of assigning the [PER] label to "[Washington] was born into slavery on the farm of James Burroughs" is formalized as answering the question "Which person is mentioned in the text?".

By unifying flat and nested NER under an MRC framework, we're able to gain a huge improvement on both flat and nested NER datasets, which achives SOTA results.

We use MRC-NER to denote the proposed framework.
Here are some of the highlights:

1. MRC-NER works better than BERT-Tagger with less training data.

2. MRC-NER is capable of handling both flat and nested NER tasks under a unified framework.

3. MRC-NER has a better zero-shot learning ability which can predicts labels unseen from the training set.

4. The query encodes prior information about the entity category to extract and has the potential to disambiguate similar classes.

Experimental Results on Flat/Nested NER Datasets

Experiments are conducted on both Flat and Nested NER datasets. The proposed method achieves vast amount of performance boost over current SOTA models.
We only list comparisons between our proposed method and previous SOTA in terms of span-level micro-averaged F1-score here. For more comparisons and span-level micro Precision/Recall scores, please check out our paper.

Flat NER Datasets

Evaluations are conducted on the widely-used bechmarks: CoNLL2003, OntoNotes 5.0 for English; MSRA, OntoNotes 4.0 for Chinese. We achieve new SOTA results on OntoNotes 5.0, MSRA and OntoNotes 4.0, and comparable results on CoNLL2003.

Dataset	Eng-OntoNotes5.0	Zh-MSRA	Zh-OntoNotes4.0
Previous SOTA	89.16	95.54	80.62
Our method	91.11	95.75	82.11
	(+1.95)	(+0.21)	(+1.49)

Nested NER Datasets

Evaluations are conducted on the widely-used ACE 2004, ACE 2005, GENIA, KBP-2017 English datasets.

Dataset	ACE 2004	ACE 2005	GENIA	KBP-2017
Previous SOTA	84.7	84.33	78.31	74.60
Our method	85.98	86.88	83.75	80.97
	(+1.28)	(+2.55)	(+5.44)	(+6.37)

Previous SOTA:

• DYGIE for ACE 2004.
• Seq2Seq-BERT for ACE 2005 and GENIA.
• ARN for KBP2017.

Data Preparation

We release preprocessed and source data files for both flat and nested NER benchmarks.

You can download the preprocessed datasets and source data files from Google Drive.

NER datasets in the CoNLL-2003 format with BMESO scheme can not be directly used in our project. Code for transforming datasets with BMESO labels to query-answer pairs is at data/data_generate/preprocess_data.py. Queries could be found at data/data_generate/query_map.py. The code assumes that each dataset after preprocessing lives in a folder containing query_ner.train, query_ner.dev, query_ner.test files under MRC framework.

Dependencies

• Packages dependencies:

python >= 3.6
PyTorch == 1.1.0 
pytorch-pretrained-bert == 0.6.1 

• Download and unzip BERT-Large, Cased English and BERT-Base, Chinese pretrained checkpoints. Then follow the guideline from huggingface to convert TF checkpoints to PyTorch.

Usage

You can directly use the following commands to train and evaluate your model with some minor changes.
As an example, the following command trains (and evaluates, automatically done after training) the BERT-MRC on English OntoNotes5.0 and Chinese Resume:

data_dir=/data/work/english_ontonotes
base_path=/home/work/mrc-for-flat-nested-ner
config_path=/home/work/mrc-for-flat-nested-ner/configs/eng_large_case_bert.json
bert_model=/data/BERT_BASE_DIR/cased_L-24_H-1024_A-16

seed=3306
task_name=ner
max_seq_length=150
num_train_epochs=4
warmup_proportion=-1
data_sign=en_onto
checkpoint=28000

gradient_accumulation_steps=4
learning_rate=6e-6
train_batch_size=36
dev_batch_size=72
test_batch_size=72
export_model=/data/export_folder/${train_batch_size}_lr${learning_rate}
output_dir=${export_model}


CUDA_VISIBLE_DEVICES=2 python3 ${base_path}/run/run_query_ner.py \
--config_path ${config_path} \
--data_dir ${data_dir} \
--bert_model ${bert_model} \
--max_seq_length ${max_seq_length} \
--train_batch_size ${train_batch_size} \
--dev_batch_size ${dev_batch_size} \
--test_batch_size ${test_batch_size} \
--checkpoint ${checkpoint} \
--learning_rate ${learning_rate} \
--num_train_epochs ${num_train_epochs} \
--warmup_proportion ${warmup_proportion} \
--export_model ${export_model} \
--output_dir ${output_dir} \
--data_sign ${data_sign} \
--gradient_accumulation_steps ${gradient_accumulation_steps} 

data_dir=/data/work/nl_resume_ner
base_path=/home/work/mrc-for-flat-nested-ner
config_path=/home/work/mrc-for-flat-nested-ner/configs/zh_bert.json
bert_model=/data/nfsdata/nlp/BERT_BASE_DIR/chinese_L-12_H-768_A-12

seed=3306
task_name=ner
max_seq_length=512
num_train_epochs=3
warmup_proportion=-1
data_sign=resume 
checkpoint=200

gradient_accumulation_steps=4
learning_rate=6e-5
train_batch_size=16
dev_batch_size=72
test_batch_size=72
export_model=/data/export_folder/${train_batch_size}_lr${learning_rate}
output_dir=${export_model}


CUDA_VISIBLE_DEVICES=0 python3 ${base_path}/run/run_query_ner.py \
--config_path ${config_path} \
--data_dir ${data_dir} \
--bert_model ${bert_model} \
--max_seq_length ${max_seq_length} \
--train_batch_size ${train_batch_size} \
--dev_batch_size ${dev_batch_size} \
--test_batch_size ${test_batch_size} \
--checkpoint ${checkpoint} \
--learning_rate ${learning_rate} \
--num_train_epochs ${num_train_epochs} \
--warmup_proportion ${warmup_proportion} \
--export_model ${export_model} \
--output_dir ${output_dir} \
--data_sign ${data_sign} \
--gradient_accumulation_steps ${gradient_accumulation_steps} 

FAQ