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.

翻译：理解科学领域的基本概念与趋势对于追踪其持续演进至关重要。本研究提出一个系统性框架，利用因果发现与推断技术分析学科研究主题的演化过程。我们定义了三种变量以涵盖自然语言处理领域研究主题演化的不同维度，并采用因果发现算法通过观测数据揭示这些变量间的因果关联。随后，我们借助该结构量化各关系间的关联强度。通过在ACL Anthology语料库开展大量实验，我们证实该框架能有效揭示广泛自然语言处理研究主题的演变趋势及深层成因。具体而言，实验表明任务与方法在自然语言处理研究中起主导驱动作用，数据集紧随其后，而评价指标的影响最小。