We make use of the best model configuration established in recent work on NER for historical documents. We use the model from the paper "Transfer learning for historical corpora: An assessment on post-OCR correction and named entity recognition", which can be found here and more specifically only the Named Entity Recognition part of it.
The text representation layer combines a variety of embeddings, including character-level embeddings and those produced from trained BERT models. Here we use BERTje, a state-of-the-art Dutch version of BERT. All embeddings are concatenated and followed by a Bi-LSTM-CRF layer.
We use and compare single-task and multi-task approaches. The former focuses on learning one entity type at a time, whereas the latter combines these tasks into a single model.
See environment.yml for a full list of modules and libraries that were used during development, as well as a way to restore this environment using Conda.
Due to the generalization of the framework, you need ot specify the challenge which you are executing, in this case named-entity-recognition and the configuration - bi-lstm-crf. The main file to be run is run.py.
For a full list of supported arguments, please refer to the original repository documentation, found here and the NER specific arguments found here
The original run configurations can be found in the launch file. Furthermore, job files used to train and evaluate the model configurations can also be found in jobs directory.
We generate machine-readable IOB format from the original annotations using the notebook process_data.ipynb. Original processed and split data can be found in the processed_data folder. We use 70-10-20 ratios for Training-Dev-Test sets respectively.