CLNER

The code is for our ACL-IJCNLP 2021 paper: Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning

CLNER is a framework for improving the accuracy of NER models through retrieving external contexts, then use the cooperative learning approach to improve the both input views. The code is initially based on flair version 0.4.3. Then the code is extended with knwoledge distillation and ACE approaches to distill smaller models or achieve SOTA results. The config files in these repos are also applicable to this code.

Guide

• Requirements
• Datasets
• Training
• Parse files
• Config File
• Citing Us
• Contact

Requirements

The project is based on PyTorch 1.1+ and Python 3.6+. To run our code, install:

pip install -r requirements.txt

The following requirements should be satisfied:

• transformers: 3.0.0

Datasets

The datasets used in our paper are available here.

Training

Training NER Models with External Contexts

Run:

CUDA_VISIBLE_DEVICES=0 python train.py --config config/wnut17_doc.yaml

Training NER Models with Cooperative Learning

Run:

CUDA_VISIBLE_DEVICES=0 python train.py --config config/wnut17_doc_cl_kl.yaml
CUDA_VISIBLE_DEVICES=0 python train.py --config config/wnut17_doc_cl_l2.yaml

Train on Your Own Dataset

To set the dataset manully, you can set the dataset in the $config_file by:

targets: ner
ner:
  Corpus: ColumnCorpus-1
  ColumnCorpus-1: 
    data_folder: datasets/conll_03_english
    column_format:
      0: text
      1: pos
      2: chunk
      3: ner
    tag_to_bioes: ner
  tag_dictionary: resources/taggers/your_ner_tags.pkl

The tag_dictionary is a path to the tag dictionary for the task. If the path does not exist, the code will generate a tag dictionary at the path automatically. The dataset format is: Corpus: $CorpusClassName-$id, where $id is the name of datasets (anything you like). You can train multiple datasets jointly. For example:

Please refer to Config File for more details.

Parse files

If you want to parse a certain file, add train in the file name and put the file in a certain $dir (for example, parse_file_dir/train.your_file_name). Run:

CUDA_VISIBLE_DEVICES=0 python train.py --config $config_file --parse --target_dir $dir --keep_order

The format of the file should be column_format={0: 'text', 1:'ner'} for sequence labeling or you can modifiy line 232 in train.py. The parsed results will be in outputs/. Note that you may need to preprocess your file with the dummy tags for prediction, please check this issue for more details.

Config File

The config files are based on yaml format.

• targets: The target task
  • ner: named entity recognition
  • upos: part-of-speech tagging
  • chunk: chunking
  • ast: abstract extraction
  • dependency: dependency parsing
  • enhancedud: semantic dependency parsing/enhanced universal dependency parsing
• ner: An example for the targets. If targets: ner, then the code will read the values with the key of ner.
  • Corpus: The training corpora for the model, use : to split different corpora.
  • tag_dictionary: A path to the tag dictionary for the task. If the path does not exist, the code will generate a tag dictionary at the path automatically.
• target_dir: Save directory.
• model_name: The trained models will be save in $target_dir/$model_name.
• model: The model to train, depending on the task.
  • FastSequenceTagger: Sequence labeling model. The values are the parameters.
  • SemanticDependencyParser: Syntactic/semantic dependency parsing model. The values are the parameters.
• embeddings: The embeddings for the model, each key is the class name of the embedding and the values of the key are the parameters, see flair/embeddings.py for more details. For each embedding, use $classname-$id to represent the class. For example, if you want to use BERT and M-BERT for a single model, you can name: TransformerWordEmbeddings-0, TransformerWordEmbeddings-1.
• trainer: The trainer class.
  • ModelFinetuner: The trainer for fine-tuning embeddings or simply train a task model without ACE.
  • ReinforcementTrainer: The trainer for training ACE.
• train: the parameters for the train function in trainer (for example, ReinforcementTrainer.train()).

Citing Us

If you feel the code helpful, please cite:

@inproceedings{wang2021improving,
    title = "{{Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning}}",
    author={Wang, Xinyu and Jiang, Yong and Bach, Nguyen and Wang, Tao and Huang, Zhongqiang and Huang, Fei and Tu, Kewei},
    booktitle = "{the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (\textbf{ACL-IJCNLP 2021})}",
    month = aug,
    year = "2021",
    publisher = "Association for Computational Linguistics",
}

Contact

Feel free to email your questions or comments to issues or to Xinyu Wang.