Code and data for our paper Retrieving Complex Tables with Multi-Granular Graph Representation Learning at SIGIR 2021. Refactor Code for table type classfication and table structure recognition.
pip install torch==1.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install dgl-cu111 dglgo -f https://data.dgl.ai/wheels/repo.html
pip insatll git+https://github.com/facebookresearch/fastText
pip install transformers pandas networkx tqdm sklearnDownload pretrained word vectors:
wget https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.en.zip
unzip wiki.en.zip
# (move unziped files to models dir)Install trec_eval tool:
git clone https://github.com/usnistgov/trec_eval.git
cd trec_eval
make
# (move trec_eval to bin dir)To run cross validation on WikiTables dataset:
python run.py --exp cross_validation --config configs/wikitables.jsonTo train & test on TTC dataset:
python run.py --exp train_test_ttc --config configs/tabletype.jsonIf you use our code in your research, please cite our work:
@inproceedings{wang2021retrieving,
title={Retrieving Complex Tables with Multi-Granular Graph Representation Learning},
author={Wang, Fei and Sun, Kexuan and Chen, Muhao and Pujara, Jay and Szekely, Pedro},
booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages={1472--1482},
year={2021}
}