A Python implementation of the approach proposed in
[1] Phong Le and Ivan Titov (2019). Boosting Entity Linking Performance by Leveraging Unlabeled Documents. ACL 2019.
Written and maintained by Phong Le (lephong.xyz [at] gmail.com)
Requirements: Python 3.7, Pytorch 0.4, CUDA 8
The following instruction is for replicating the experiments reported in [1]. Note that training and testing take lots of RAM (about 30GB) because some files related to Freebase have to be loaded.
Download data from here and unzip to the main folder (i.e. your-path/wnel).
To train, from the main folder run
python3 -u -m nel.main --mode train --inference star --multi_instance --n_negs 5 --margin 0.1 --n_rels 1 --eval_after_n_epochs 6 --n_epochs 6 --ent_top_n 30 --preranked_data data/generated/test_train_data/preranked_all_datasets_50kRCV1_large --n_not_inc 50 --n_docs 50000
IMPORTANT: if you want to train the model from scratch, you have to remove the current saved model (if exists, by rm model.*).
Using a GTX 1080 Ti GPU it will take about 30 minutes. The output is a model saved in two files:
model.config and model.state_dict .
For more options, please have a look at nel/main.py