Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment in Diverse Domains

This is the source code from the COLING paper:

Jeremy Barnes, Roman Klinger, and Sabine Schulde im Walde. 2018. Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment in Diverse Domains. In Proceedings of COLING 2018 (to appear).

If you use the code for academic research, please cite the paper in question:

@inproceedings{Barnes2018domain,
    author={Barnes, Jeremy and Klinger, Roman and Schulte im Walde, Sabine},
    title={Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment in Diverse Domains},
    booktitle = {Proceedings of COLING 2018, the 27th International Conference on Computational Linguistics},
    year = {2018},
    month = {August},
    address = {Santa Fe, USA},
    publisher = {Association for Computational Linguistics},
    language = {english}
    }

Requirements to run the experiments

• Python 3
• NumPy
• sklearn [http://scikit-learn.org/stable/]
• pytorch [http://pytorch.org/]

Usage

First, clone the repo:

git clone https://github.com/jbarnesspain/domain_blse
cd blse

Then, get the embeddings, either by training your own, or by downloading the pretrained embeddings mentioned in the paper, and put them in the 'embeddings' directory:

Run the domain_blse script:

python3 BLSE_domain_all.py

Finally, you can use the blse.py script which will automatically use the best hyperparameters found.

python3 blse.py

License

Copyright (C) 2018, Jeremy Barnes

Licensed under the terms of the Creative Commons CC-BY public license