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https://github.com/dmis-lab/gener

Simple Questions Generate Named Entity Recognition Datasets (EMNLP 2022)
https://github.com/dmis-lab/gener

dataset named-entity-recognition ner nlp

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Simple Questions Generate Named Entity Recognition Datasets (EMNLP 2022)

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README 

        
# GeNER

This repository provides the official code for **GeNER** (an automated dataset **Ge**neration framework for **NER**).



### Updates



* **Nov. 2022:** Synthetic datasets generated by GeNER can be downloaded (see below).

```bash

wget http://nlp.dmis.korea.edu/projects/gener-kim-et-al-2022/synthetic_datasets-221105.zip

```

* **Nov. 2022:** Several hyperparameters have been updated for better reproducibility. Please check out the changes in Makefile and configuration files.

* **Oct. 2022:** Our paper (GeNER) is accepted to [EMNLP 2022](https://2022.emnlp.org/)! Camera-ready version is now available online ([link](https://arxiv.org/abs/2112.08808)).



## Overview of GeNER



GeNER allows you to build NER models for specific entity types of interest. The core idea is to **ask simple natural language questions** to an open-domain question answering (QA) system and then **retrieve phrases and sentences**, as shown in the query formulation and retrieval stages in the figure below. Please see our paper ([Simple Questions Generate Named Entity Recognition Datasets](https://arxiv.org/abs/2112.08808)) for details.





      




## Requirements



Please follow the instructions below to set up your environment and install GeNER.



```bash

# Create a conda virtual environment

conda create -n GeNER python=3.8

conda activate GeNER



# Install PyTorch v1.9.0

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html



# Install GeNER

git clone https://github.com/dmis-lab/GeNER.git

cd GeNER

pip install -r requirements.txt

```



### NER Benchmarks



Run `unzip data/benchmarks.zip -d ./data` to unpack (pre-processed) NER benchmarks.



### QA Model and Phrase Index: DensePhrases



We use DensePhrases and a Wikipedia index precomputed by DensePhrases in order to automatically generate NER datasets.

After installing [DensePhrases v1.0.0](https://github.com/princeton-nlp/DensePhrases#installation), please download **the DensePhrases model** (densephrases-multi-query-multi) and **the phrase index** (densephrases-multi_wiki-20181220) in the official DensePhrases repository.



* [[GitHub](https://github.com/princeton-nlp/DensePhrases)] [[Paper](https://arxiv.org/abs/2012.12624)] [[Demo](http://densephrases.korea.ac.kr/)]



### AutoPhrase (Optional)



Using AutoPhrase in the dictionary matching stage usually improves final NER performance.

If you are using AutoPhrase to apply Rule 10 (i.e., refining entity boundaries), please check the system requirements in the AutoPhrase repository.

If you are not using AutoPhrase, set `refine_boundary` to `false` in a configuration file in the `configs` directory.



* [[GitHub](https://github.com/shangjingbo1226/AutoPhrase)] [[Paper](https://arxiv.org/abs/1702.04457)]



### Computational Resource

Please see the resource requirement of DensePhrases and self-training, and check available resources of your machine. 



* **100GB RAM** and **a single 11G GPU** to run DensePhrases 

* **Single 6G GPU** to perform self-training (based on batch size 16)



## Reproducing Experiments



GeNER is implemented as a pipeline of DensePhrases, dictionary matching, and AutoPhrase.

The entire pipeline is controlled by configuration files located in the `configs` directory.

Please see `configs/README.md` for details.



We have already set up configuration files and optimal hyperparameters for all benchmarks and experiments so that you can easily reproduce similar or better performance to those presented in our paper. Just follow the instructions below for reproduction!



### Example: low-resource NER (CoNLL-2003)



This example is intended to reproduce the experiment in the low-resource NER setting on the CoNLL-2003 benchmark. If you want to reproduce other experiments, you will need to change some arguments including `--gener_config_path` according to the target benchmark.



#### Retrieval



Running `retrieve.py` will create `*.json` and `*.raw` files in the `data/retrieved/conll-2003` directory.



```bash

export CUDA_VISIBLE_DEVICES=0

export DENSEPHRASES_PATH={enter your densephrases path here}

export CONFIG_PATH=./configs/conll-2003.json



python retrieve.py \

      --run_mode eval \

      --model_type bert \

      --cuda \

      --aggregate \

      --truecase \

      --return_sent \

      --pretrained_name_or_path SpanBERT/spanbert-base-cased \

      --dump_dir $DENSEPHRASES_PATH/outputs/densephrases-multi_wiki-20181220/dump/ \

      --index_name start/1048576_flat_OPQ96  \

      --load_dir $DENSEPHRASES_PATH/outputs/densephrases-multi-query-multi/  \

      --gener_config_path $CONFIG_PATH

```



#### Applying AutoPhrase (optional)



`apply_autophrase.sh` takes as input all `*.raw` files in the `data/retrieved/conll-2003` directory and outputs `*.autophrase` files in the same directory.



```bash

bash autophrase/apply_autophrase.sh data/retrieved/conll-2003

```



#### Dictionary matching



Running `annotate.py` will create `train_hf.json` in the `data/annotated/conll-2003` directory.



```bash

python annotate.py --gener_config_path $CONFIG_PATH

```



#### Self-training



Finally, you can get the final NER model and see its performance. 

Running `make conll-low` creates the `train.json` file in the `data/annotated/conll-2003` directory, which is pre-processed and derived from `train_tf.json`.

The model and training logs are stored in the `./outputs` directory. See the Makefile file for running experiments on other benchmarks.



```bash

make conll-low

```



## Fine-tuning GeNER



While GeNER performs well without any human-labeled data, you can further boost GeNER's performance using some training examples.

The way to do this is very simple: load a trained GeNER model from the `./outputs` directory and fine-tune it on training examples you have by a standard NER objective (i.e., token classification).

We provide a fine-tuning **script** in this repository (`self-training/run_ner.py`) and **datasets** to reproduce fine-grained and few-shot NER experiments (`data/fine-grained` and `data/few-shot` directories).



```bash

export CUDA_VISIBLE_DEVICES=0



python self_training/run_ner.py \

      --train_dir data/few-shot/conll-2003/conll-2003_0 \

      --eval_dir data/few-shot/conll-2003/conll-2003_0 \

      --model_type roberta \

      --model_name_or_path outputs/{enter GeNER model path here} \

      --output_dir outputs/{enter GeNER model path here} \

      --num_train_epochs 100 \

      --per_gpu_train_batch_size 64 \

      --per_gpu_eval_batch_size 64 \

      --learning_rate 1e-5 \

      --do_train \

      --do_eval \

      --do_predict \

      --evaluate_during_training



# Note that this hyperparameter setup may not be optimal. It is recommended to search for more effective hyperparameters, especially the learning rate.

```



## Building NER Models for Your Specific Needs



The main benefit of GeNER is that you can create NER datasets of new and different entity types you want to extract.

Suppose you want to extract fighter aircraft names.

The first thing you have to do is to formulate your needs as natural language questions such as "Which fighter aircraft?."

At this stage, we recommend using the [DensePhrases demo](http://densephrases.korea.ac.kr/) to manually check the feasibility of your questions.

If relevant phrases are retrieved well, you can proceed to the next step.



Next, you should make a configuration file (e.g., fighter_aircraft_config.json) and set up its values.

You can reflect questions you made in the configuration file as follows: `"subtype": "fighter aircraft"`.

Also, you can fine-tune some hyperparameters such as `top_k` and normalization rules.

See `configs/README.md` for detailed descriptions of configuration files.



```bash

{

    "retrieved_path": "data/retrieved/{file name}",

    "annotated_path": "data/annotated/{file name}",

    "add_abbreviation": true,

    "refine_boundary" : true,

    "subquestion_configs": [

        {

            "type": "{the name of pre-defined entity type}",

            "subtype" : "fighter aircraft",

            "top_k" : 5000,

            "split_composite_mention": true,

            "remove_lowercase_phrase": true,

            "remove_the": false,

            "skip_lowercase_ngram": 1

        }

    ]

}

```



For subsequent steps (i.e., retrieval, dictionary matching, and self-training), refer to the CoNLL-2003 example described above.



## References



Please cite our paper if you consider GeNER to be related to your work. Thanks!



```bibtex

@inproceedings{kim-etal-2022-simple,

    title = "Simple Questions Generate Named Entity Recognition Datasets",

    author = "Kim, Hyunjae  and

      Yoo, Jaehyo  and

      Yoon, Seunghyun  and

      Lee, Jinhyuk  and

      Kang, Jaewoo",

    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",

    month = dec,

    year = "2022",

    address = "Abu Dhabi, United Arab Emirates",

    publisher = "Association for Computational Linguistics",

    url = "https://aclanthology.org/2022.emnlp-main.417",

    pages = "6220--6236"

}

```



## Contact



Feel free to email Hyunjae Kim `([email protected])` if you have any questions.



## License



See the LICENSE file for details.