@inproceedings{ash-etal-2021-machine,
    title = "Machine Extraction of Tax Laws from Legislative Texts",
    author = "Ash, Elliott  and
      Guillot, Malka  and
      Han, Luyang",
    editor = "Aletras, Nikolaos  and
      Androutsopoulos, Ion  and
      Barrett, Leslie  and
      Goanta, Catalina  and
      Preotiuc-Pietro, Daniel",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.nllp-1.7/",
    doi = "10.18653/v1/2021.nllp-1.7",
    pages = "76--85",
    abstract = "Using a corpus of compiled codes from U.S. states containing labeled tax law sections, we train text classifiers to automatically tag tax-law documents and, further, to identify the associated revenue source (e.g. income, property, or sales). After evaluating classifier performance in held-out test data, we apply them to an historical corpus of U.S. state legislation to extract the flow of relevant laws over the years 1910 through 2010. We document that the classifiers are effective in the historical corpus, for example by automatically detecting establishments of state personal income taxes. The trained models with replication code are published at \url{https://github.com/luyang521/tax-classification}."
}