wnel: A weakly supervised learning approach to entity linking

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)

Installation

Requirements: Python 3.7, Pytorch 0.4, CUDA 8

Usage

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.

Data

Download data from here and unzip to the main folder (i.e. your-path/wnel).

Train

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