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A Sea of Words: An In-Depth Analysis of Anchors for Text Data

Official code for the paper "A Sea of Words: An In-Depth Analysis of Anchors for Text Data", AISTATS 2023.

Dependencies

The required Python packages are listed in requirements.txt.

Install the dependencies using the following commands:

pip install -r requirements.txt
python -m spacy download en_core_web_sm

Usage

  • exhaustive_anchors contains -- empirical_anchor_text.py: our exhaustive-empirical implementation of Anchors for text data -- similarity.py: script to compute the Jaccard similarity between exhaustive-empirical Anchors and official implementation
  • in monte-carlo, the script precision_approximation.py validates Approximating the precision of a linear classifier, norm_tf_idf.py validates the statement for the normalized TF-IDF vectorization.
  • analysis contains experiments for linear models (linear_model), if-then rules (simple_rules), and neural networks (nn_gradient). Their results are available in results.
  • run generate_figures.py to visualize some experiments of Analysis on explainable classifiers: figures will be saved in results.

Citation

If you use this code or find our work helpful, please cite our paper:

@InProceedings{lopardo23anchors,
  title={A Sea of Words: An In-Depth Analysis of Anchors for Text Data},
  author={Lopardo, Gianluigi and Precioso, Frederic and Garreau, Damien},
  booktitle={Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
  pages = {4848--4879},
  year = {2023},
  volume = {206},
  series = {Proceedings of Machine Learning Research},
  pdf = {https://proceedings.mlr.press/v206/lopardo23a/lopardo23a.pdf},
  url = {https://proceedings.mlr.press/v206/lopardo23a.html},
}