Year: 2025
Type: conference-paper
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 ∼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, datadriven literature surveys. 1
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Funder: Deutsche Forschungsgemeinschaft
Award: G8779138187
OpenAlex ID: W4412889945