# Examining Citations of Natural Language Processing Literature

arXiv: https://arxiv.org/abs/2005.00912 · HTML: https://mlsanity.com/paper/2005.00912/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 20 of 20)

- [What Do NLP Researchers Believe? Results of the NLP Community Metasurvey](https://mlsanity.com/paper/2208.12852/) (2022)
- [The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research](https://mlsanity.com/paper/2305.02797/) (2023)
- [We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields](https://mlsanity.com/paper/2310.14870/) (2023)
- [From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP](https://mlsanity.com/paper/2406.12618/) (2024)
- [A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?](https://mlsanity.com/paper/2305.12920/) (2023)
- [Forgotten Knowledge: Examining the Citational Amnesia in NLP](https://mlsanity.com/paper/2305.18554/) (2023)
- [The Nature of NLP: Analyzing Contributions in NLP Papers](https://mlsanity.com/paper/2409.19505/) (2024)
- [D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research](https://mlsanity.com/paper/2204.13384/) (2022)
- [Exploring the Landscape of Natural Language Processing Research](https://mlsanity.com/paper/2307.10652/) (2023)
- [Determining the Credibility of Science Communication](https://mlsanity.com/paper/2105.14473/) (2021)
- [To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing](https://mlsanity.com/paper/2310.07715/) (2023)
- [Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science](https://mlsanity.com/paper/2409.19508/) (2024)
- [Analyzing 16,193 LLM Papers for Fun and Profits](https://mlsanity.com/paper/2504.08619/) (2025)
- [Assisting Decision Making in Scholarly Peer Review: A Preference Learning Perspective](https://mlsanity.com/paper/2109.01190/) (2021)
- [What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models](https://mlsanity.com/paper/2407.01929/) (2024)
- [The Role of Computing Resources in Publishing Foundation Model Research](https://mlsanity.com/paper/2510.13621/) (2025)
- [Who should I Collaborate with? A Comparative Study of Academia and Industry Research Collaboration in NLP](https://mlsanity.com/paper/2308.04524/) (2023)
- [Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias](https://mlsanity.com/paper/2512.05714/) (2025)
- [The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing](https://mlsanity.com/paper/2607.02416/) (2026)
- [Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systems](https://mlsanity.com/paper/2608.09964/) (2026)

## Abstract (arXiv)

We extracted information from the ACL Anthology (AA) and Google Scholar (GS) to examine trends in citations of NLP papers. We explore questions such as: how well cited are papers of different types (journal articles, conference papers, demo papers, etc.)? how well cited are papers from different areas of within NLP? etc. Notably, we show that only about 56% of the papers in AA are cited ten or more times. CL Journal has the most cited papers, but its citation dominance has lessened in recent years. On average, long papers get almost three times as many citations as short papers; and papers on sentiment classification, anaphora resolution, and entity recognition have the highest median citations. The analyses presented here, and the associated dataset of NLP papers mapped to citations, have a number of uses including: understanding how the field is growing and quantifying the impact of different types of papers.