figet-hyperbolic-space
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Code for the paper "Fine-Grained Entity Typing in Hyperbolic Space"
Fine-Grained Entity Typing in Hyperbolic Space
Code for the paper "Fine-Grained Entity Typing in Hyperbolic Space" published at RepL4NLP @ ACL 2019
Model overview:
Citation
The source code and data in this repository aims at facilitating the study of fine-grained entity typing. If you use the code/data, please cite it as follows:
@inproceedings{lopez-etal-2019-fine,
title = "Fine-Grained Entity Typing in Hyperbolic Space",
author = "L{\'o}pez, Federico and
Heinzerling, Benjamin and
Strube, Michael",
booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W19-4319",
pages = "169--180",
}
Dependencies
-
PyTorch 1.1
-
tqdm
-
tensorboardX
-
pyflann
A conda environment can be created as well from the environment.yml
file.
To embed the graphs into the different metric spaces the library Hype was used.
Running the code
1. Download data
Download and uncompress Ultra-Fine dataset and GloVe word embeddings:
./scripts/figet.sh get_data
2. Preprocess data
The parameter freq-sym
can be replaced to store different preprocessing configurations:
./scripts/figet.sh preprocess freq-sym
3. Train model
The name of the preprocessing used in the previous step must be given as a parameter.
./scripts/figet.sh train freq-sym
3. Do inference
./scripts/figet.sh inference freq-sym
Acknowledgements
We thank to Choi et al for the release of the Ultra-Fine dataset and their model.
License
MIT