Code for the paper "Fine-Grained Entity Typing in Hyperbolic Space" published at RepL4NLP @ ACL 2019

Model overview:

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",
}

• 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.

Download and uncompress Ultra-Fine dataset and GloVe word embeddings:

./scripts/figet.sh get_data

The parameter freq-sym can be replaced to store different preprocessing configurations:

./scripts/figet.sh preprocess freq-sym

The name of the preprocessing used in the previous step must be given as a parameter.

./scripts/figet.sh train freq-sym

./scripts/figet.sh inference freq-sym

We thank to Choi et al for the release of the Ultra-Fine dataset and their model.

MIT