被遗忘的知识：审视NLP中的引用遗忘现象
Forgotten Knowledge: Examining the Citational Amnesia in NLP
AI总结：
本研究分析了约71.5K篇NLP论文，揭示被引论文年龄多样性在2014年后逆转降至历史最低，且高引论文引用多样性最低，加剧了对近期研究的过度关注。
摘要：
引用论文是现代科学写作讨论和借鉴过往研究的主要方法。从整体上看，引用不同时期和研究领域的多样化论文，是衡量学界阅读广度的一个指标。然而，目前鲜有研究关注引用的广泛时间模式。本研究系统地实证考察了以下问题：我们在引用论文时倾向于追溯多远的历史？这一情况随时间发生了怎样的变化？哪些因素与这种引用关注/遗忘现象相关？我们选择NLP作为研究兴趣领域，分析了约71.5K篇论文，以展示并量化引用中的几个关键趋势。值得注意的是，约62%的被引论文发表于出版前最近的五年内，而仅有约17%的被引论文超过十年历史。此外，我们发现被引论文的中位年龄和年龄多样性在1990年至2014年间稳步增长，但自那以后趋势逆转，当前的NLP论文在时间引用多样性上处于历史最低水平。最后，我们表明，与20世纪90年代不同，过去十年中被高引的论文也是引用多样性最低的论文，这可能是导致对近期研究强烈（且可以说有害的）关注的原因。代码、数据和演示可在项目主页获取。
英文摘要：
Citing papers is the primary method through which modern scientific writing discusses and builds on past work. Collectively, citing a diverse set of papers (in time and area of study) is an indicator of how widely the community is reading. Yet, there is little work looking at broad temporal patterns of citation. This work systematically and empirically examines: How far back in time do we tend to go to cite papers? How has that changed over time, and what factors correlate with this citational attention/amnesia? We chose NLP as our domain of interest and analyzed approximately 71.5K papers to show and quantify several key trends in citation. Notably, around 62% of cited papers are from the immediate five years prior to publication, whereas only about 17% are more than ten years old. Furthermore, we show that the median age and age diversity of cited papers were steadily increasing from 1990 to 2014, but since then, the trend has reversed, and current NLP papers have an all-time low temporal citation diversity. Finally, we show that unlike the 1990s, the highly cited papers in the last decade were also papers with the least citation diversity, likely contributing to the intense (and arguably harmful) recency focus. Code, data, and a demo are available on the project homepage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
