@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://aclanthology.org/2025.acl-long.1224/",
doi = "10.18653/v1/2025.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."
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%0 Conference Proceedings
%T The Nature of NLP: Analyzing Contributions in NLP Papers
%A Pramanick, Aniket
%A Hou, Yufang
%A Mohammad, Saif M.
%A Gurevych, Iryna
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F pramanick-etal-2025-nature
%X 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 ~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.
%R 10.18653/v1/2025.acl-long.1224
%U https://aclanthology.org/2025.acl-long.1224/
%U https://doi.org/10.18653/v1/2025.acl-long.1224
%P 25169-25191
Markdown (Informal)
[The Nature of NLP: Analyzing Contributions in NLP Papers](https://aclanthology.org/2025.acl-long.1224/) (Pramanick et al., ACL 2025)
ACL
- Aniket Pramanick, Yufang Hou, Saif M. Mohammad, and Iryna Gurevych. 2025. The Nature of NLP: Analyzing Contributions in NLP Papers. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 25169–25191, Vienna, Austria. Association for Computational Linguistics.