@inproceedings{thorne-akhondi-2024-nlp,
    title = "{NLP} for Chemistry {--} Introduction and Recent Advances",
    author = "Thorne, Camilo  and
      Akhondi, Saber",
    editor = "Klinger, Roman  and
      Okazaki, Naozaki  and
      Calzolari, Nicoletta  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.lrec-tutorials.8/",
    pages = "45--49",
    abstract = "In this half-day tutorial we will be giving an introductory overview to a number of recent applications of natural language processing to a relatively underrepresented application domain: chemistry. Specifically, we will see how neural language models (transformers) can be applied (oftentimes with near-human performance) to chemical text mining, reaction extraction, or more importantly computational chemistry (forward and backward synthesis of chemical compounds). At the same time, a number of gold standards for experimentation have been made available to the research {--}academic and otherwise{--} community. Theoretical results will be, whenever possible, supported by system demonstrations in the form of Jupyter notebooks. This tutorial targets an audience interested in bioinformatics and biomedical applications, but pre-supposes no advanced knowledge of either."
}