@inproceedings{lalor-etal-2025-textagon,
    title = "Textagon: Boosting Language Models with Theory-guided Parallel Representations",
    author = "Lalor, John P.  and
      Qin, Ruiyang  and
      Dobolyi, David  and
      Abbasi, Ahmed",
    editor = "Mishra, Pushkar  and
      Muresan, Smaranda  and
      Yu, Tao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.9/",
    pages = "82--92",
    ISBN = "979-8-89176-253-4",
    abstract = "Pretrained language models have significantly advanced the state of the art in generating distributed representations of text. However, they do not account for the wide variety of available expert-generated language resources and lexicons that explicitly encode linguistic/domain knowledge. Such lexicons can be paired with learned embeddings to further enhance NLP prediction and linguistic inquiry. In this work we present Textagon, a Python package for generating parallel representations for text based on predefined lexicons and selecting representations that provide the most information. We discuss the motivation behind the software, its implementation, as well as two case studies for its use to demonstrate operational utility."
}