The Nature of NLP: Analyzing Contributions in NLP Papers

2024-09-29

arXiv.org

AI Summary

The lack of quantitative characterization and systematic definition of research contributions in NLP hinders rigorous analysis of scientific progress. Method: We introduce NLPContributions, the first manually annotated dataset comprising 2,000 paper abstracts, and propose the first fine-grained, extensible taxonomy of NLP research contributions. We formally define the novel task of “contribution sentence identification and classification” and develop a hybrid approach combining rule-based heuristics with supervised learning models to extract and classify contribution statements. Applying this framework to ~29,000 NLP papers spanning the 1970s–2020s, we conduct large-scale bibliometric and diachronic analyses. Contribution/Results: Our analysis reveals previously undocumented evolutionary patterns: a resurgence in human language–oriented research and sustained growth in methodological and data-related contributions. The dataset, taxonomy, and code are publicly released to support reproducible, longitudinal studies of field evolution.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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 $sim$$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.

Problem

• Defining and classifying research contributions in NLP papers
• Automatically identifying contribution types in NLP abstracts
• Analyzing historical trends and evolution of NLP research focus

Innovation

• Proposed taxonomy for NLP research contributions
• Created annotated dataset NLPContributions
• Introduced automated contribution classification task