@inproceedings{tsunokake-matsubara-2022-classification,
    title = "Classification of {URL} Citations in Scholarly Papers for Promoting Utilization of Research Artifacts",
    author = "Tsunokake, Masaya  and
      Matsubara, Shigeki",
    editor = "Ghosal, Tirthankar  and
      Blanco-Cuaresma, Sergi  and
      Accomazzi, Alberto  and
      Patton, Robert M.  and
      Grezes, Felix  and
      Allen, Thomas",
    booktitle = "Proceedings of the first Workshop on Information Extraction from Scientific Publications",
    month = nov,
    year = "2022",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.wiesp-1.2",
    pages = "8--19",
    abstract = "Utilizing citations for research artifacts (e.g., dataset, software) in scholarly papers contributes to efficient expansion of research artifact repositories and various applications e.g., the search, recommendation, and evaluation of such artifacts. This study focuses on citations using URLs (URL citations) and aims to identify and analyze research artifact citations automatically. This paper addresses the classification task for each URL citation to identify (1) the role that the referenced resources play in research activities, (2) the type of referenced resources, and (3) the reason why the author cited the resources. This paper proposes the classification method using section titles and footnote texts as new input features. We extracted URL citations from international conference papers as experimental data. We performed 5-fold cross-validation using the data and computed the classification performance of our method. The results demonstrate that our method is effective in all tasks. An additional experiment demonstrates that using cited URLs as input features is also effective.",
}