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：理解科学领域中的基本概念与趋势，对于紧跟其持续发展至关重要。本研究提出了一套系统性框架，借助因果发现与推断技术分析科学领域研究主题的演化过程。我们定义了三个变量来涵盖自然语言处理领域研究主题演化的不同维度，并利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，我们借助该因果结构测量上述关系的强度。通过在ACL Anthology语料库上开展广泛实验，我们证明了该框架能够有效揭示自然语言处理各类研究主题的演化趋势及其根本成因。具体而言，研究显示任务与方法才是自然语言处理研究的核心驱动力，数据集紧随其后，而评价指标的影响微乎其微。