# A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?

arXiv: https://arxiv.org/abs/2305.12920 · HTML: https://mlsanity.com/paper/2305.12920/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research](https://mlsanity.com/paper/2112.01716/) (2021)
- [Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates](https://mlsanity.com/paper/2005.00649/) (2020)
- [Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction](https://mlsanity.com/paper/1906.09317/) (2019)
- [Factors Influencing the Surprising Instability of Word Embeddings](https://mlsanity.com/paper/1804.09692/) (2018)
- [The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers](https://mlsanity.com/paper/1808.07290/) (2018)
- [TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics](https://mlsanity.com/paper/2101.10273/) (2021)
- [Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding](https://mlsanity.com/paper/2003.01747/) (2020)
- [End-to-End NLP Knowledge Graph Construction](https://mlsanity.com/paper/2106.01167/) (2021)
- [Examining Citations of Natural Language Processing Literature](https://mlsanity.com/paper/2005.00912/) (2020)
- [The State of NLP Literature: A Diachronic Analysis of the ACL Anthology](https://mlsanity.com/paper/1911.03562/) (2019)
- [Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts](https://mlsanity.com/paper/1704.07828/) (2017)

## Built on by (most cited first, 14 of 14)

- [From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP](https://mlsanity.com/paper/2406.12618/) (2024)
- [The Nature of NLP: Analyzing Contributions in NLP Papers](https://mlsanity.com/paper/2409.19505/) (2024)
- [Efficient Performance Tracking: Leveraging Large Language Models for Automated Construction of Scientific Leaderboards](https://mlsanity.com/paper/2409.12656/) (2024)
- [Awes, Laws, and Flaws From Today's LLM Research](https://mlsanity.com/paper/2408.15409/) (2024)
- [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)
- [A Position Paper on the Automatic Generation of Machine Learning Leaderboards](https://mlsanity.com/paper/2505.17465/) (2025)
- [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)
- [A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English](https://mlsanity.com/paper/2504.08609/) (2025)
- [Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework](https://mlsanity.com/paper/2604.06170/) (2026)
- [Quantifying the Knowledge Proximity Between Academic and Industry Research: An Entity and Semantic Perspective](https://mlsanity.com/paper/2602.05211/) (2026)
- [ClaimFlow: Tracing the Evolution of Scientific Claims in NLP](https://mlsanity.com/paper/2603.16073/) (2026)
- [How Does Research Evolve? Tracing Cross-Domain Trajectories in NLP, ML, and CV Through Claim-Grounded Typed Citations](https://mlsanity.com/paper/2606.22342/) (2026)
- [The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing](https://mlsanity.com/paper/2607.02416/) (2026)

## Abstract (arXiv)

Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its continuous advancement. In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. We define three variables to encompass diverse facets of the evolution of research topics within NLP and utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data. Subsequently, we leverage this structure to measure the intensity of these relationships. By conducting extensive experiments on the ACL Anthology corpus, we demonstrate that our framework effectively uncovers evolutionary trends and the underlying causes for a wide range of NLP research topics. Specifically, we show that tasks and methods are primary drivers of research in NLP, with datasets following, while metrics have minimal impact.