/NER

Project in Named Entity Recognition

Primary LanguagePython

CL_NER

Continual Learning with Structured Regularization in Named Entity Recognition

Requirements

  • PyTorch
  • numpy
  • tqdm
  • seqeval
  • gensim
  • Jupyter Notebook

Experiments

To replicate the experiments:

  1. Clone the repository.

  2. Under the folder experiments, locate the specific experiments to replicate (NA - New Addition, Seq - Sequential).

  3. Download a copy of Google word2vec pre-trained embedding, put under "NER/checkpoints/GoogleNews-vectors-negative300.bin" or specify the path in configuration of each train script.

  4. Three scripts are enlisted in each directory (Baseline vs. EWC vs. SI). Inside, "multiple_allowed" controls whether to purge sentences with multiple entity tags. Run python3 ewc_train.py for example, for training and evaluation.

Acknowledgement

The copy of CONLL 2003 dataset is referred here from huggingface.