@inproceedings{pramanick-etal-2025-nature,
    title = "The Nature of {NLP}: Analyzing Contributions in {NLP} Papers",
    author = "Pramanick, Aniket  and
      Hou, Yufang  and
      Mohammad, Saif M.  and
      Gurevych, Iryna",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1224/",
    pages = "25169--25191",
    ISBN = "979-8-89176-251-0",
    abstract = "Natural Language Processing (NLP) is an established and dynamic field. Despite this, what constitutes NLP research remains debated. In this work, we address the question by quantitatively examining NLP research papers. We propose a taxonomy of research contributions and introduce {\_}NLPContributions{\_}, a dataset of nearly $2k$ NLP research paper abstracts, carefully annotated to identify scientific contributions and classify their types according to this taxonomy. We also introduce a novel task of automatically identifying contribution statements and classifying their types from research papers. We present experimental results for this task and apply our model to {\textasciitilde}$29k$ NLP research papers to analyze their contributions, aiding in the understanding of the nature of NLP research. We show that NLP research has taken a winding path {---} with the focus on language and human-centric studies being prominent in the 1970s and 80s, tapering off in the 1990s and 2000s, and starting to rise again since the late 2010s. Alongside this revival, we observe a steady rise in dataset and methodological contributions since the 1990s, such that today, on average, individual NLP papers contribute in more ways than ever before. Our dataset and analyses offer a powerful lens for tracing research trends and offer potential for generating informed, data-driven literature surveys."
}