Here is the repo of our paper: Event Linking: Grounding Event Mentions to Wikipedia
EveLINK is built based on the code of BLINK. When using our code, please give enough credits to BLINK.
conda create -n evelink python=3.8
conda activate evelink
pip install -r requirements.txt
You may need to change torch version based on your CUDA version.
wget https://cogcomp.seas.upenn.edu/Data/event_linking/data_and_models.zip
Wikipedia domain data is in wikipedia folder.
raw is the raw data without predicted entities.
preprocessed is the data with predicted entities in the context.
The data format in raw and preprocessed is:
{ "Correct Wikipedia title":
{ "page": the page of the event mention
"start": the start character index of the event mention
"end": the end character index of the event mention
"type": verb or other (nominal)
"status": hard cases or easy cases
"entities": predicted entities in local context
}
}
wiki is the simplified Wikipedia dumps.
title_text.json is the dictionary of all the Wikipedia titles' page text.
t2hyperlinks.json is the dictionary of hyperlinks in all the Wikipedia titles.
enwiki-20200301.id2t.pkl is the mapping of wikipedia title and wikipedia ID.
NYT domain data is in nyt folder.
The data format is:
{
"context_left": context on the left of the event mention,
"mention": event mention,
"context_right": context on the right of the event mention,
"label": content of the correct title,
"label_title": the correct title,
"label_id": the correct title ID,
"hyperlinks": the hyperlinks of the correct Wikipedia page (only include first 10 hyperlinks. more in the ``t2hyperlinks.json``)
"entities": predicted entities in local context
"type": verb or other (nominal)
"status": hard cases or easy cases
}
To run our model, you need to first process all the data by running:
python data_processing.py
Then, to link event mentions to Wikipedia, you need to run:
python event_linking_main.py --predict --topk 100 --mode test
The output file will be saved in [out] folder, and you can evaluate the accuracy by running:
python event_linking_main.py --evaluate --topk 100 --mode test
You can choose mode from [test, valid, train]. If you want to evaluate on New York Times data, just add --nyt.
If you want to train a new model, please follow the listed steps:
You first need to get the data ready by running:
python data_processing.py
If you want to use different data, please convert it to the same format of the data we provide.
To train the bi-encoder, just run:
python blink/biencoder/train_biencoder.py --output_path "models/biencoder" --data_path "out/" --num_train_epochs 10 --learning_rate 1e-5 --bert_model "bert-large-uncased" --train_batch_size 64 --max_context_length 256 --max_cand_length 256 --data_parallel --print_interval 10000 --eval_interval 10000000
Before you train cross-encoder, you first need to use your trained Bi-Encoder to generate the training data. Please run:
python blink/biencoder/eval_biencoder.py --path_to_model "models/biencoder/pytorch_model.bin" --data_path "out/" --mode "valid" --bert_model "bert-large-uncased" --eval_batch_size 128 --encode_batch_size 200 --max_context_length 256 --max_cand_length 256 --output_path "out/" --cand_encode_path "models/all_entities.encoding" --cand_pool_path "models/cand_pool.t7" --save_topk_result --data_parallel
python event_linking_main.py --predict --top_k 30 --fast --mode train --save_topk_result
python event_linking_main.py --predict --top_k 30 --fast --mode valid --save_topk_result
After generating all the encodings and data, run:
python blink/crossencoder/train_cross.py --output_path "models/crossencoder" --data_path "out/" --num_train_epochs 10 --learning_rate 1e-5 --bert_model "bert-large-uncased" --train_batch_size 2 --eval_batch_size 64 --max_context_length 256 --max_cand_length 256 --max_seq_length 512 --add_linear --data_parallel --print_interval 10000 --eval_interval 10000000