Since the seminal paper by Tversky and Kahneman, the conjunction fallacy has
been the subject of multiple debates and become a fundamental challenge for
cognitive theories in decision-making. In this article, we take a rather
uncommon perspective on this phenomenon. Instead of trying to explain the
nature or causes of the conjunction fallacy (intensional definition), we
analyze its range of factual possibilities (extensional definition). We show
that the majority of research on the conjunction fallacy, according to our
sample of experiments reviewed which covers literature between 1983 and 2016,
has focused on a narrow part of the a priori factual possibilities, implying
that explanations of the conjunction fallacy are fundamentally biased by the
short scope of possibilities explored. The latter is a rather curious aspect of
the research evolution in the conjunction fallacy considering that the very
nature of it is motivated by extensional considerations.

通过分析多个实验的文献，我们发现大多数关于连词谬误的研究集中在某个狭窄的前提事实可能性上，暗示了连词谬误的解释在本质上被所研究的可能性的短视所限制。

分析联结谬误的事实

Analyizing the Conjunction Fallacy as a Fact

In this study, we delve into the dynamic landscape of machine learning
research evolution. Initially, through the utilization of Latent Dirichlet
Allocation, we discern pivotal themes and fundamental concepts that have
emerged within the realm of machine learning. Subsequently, we undertake a
comprehensive analysis to track the evolutionary trajectories of these
identified themes. To quantify the novelty and divergence of research
contributions, we employ the Kullback-Leibler Divergence metric. This
statistical measure serves as a proxy for ``surprise'', indicating the extent
of differentiation between the content of academic papers and the subsequent
developments in research. By amalgamating these insights, we gain the ability
to ascertain the pivotal roles played by prominent researchers and the
significance of specific academic venues (periodicals and conferences) within
the machine learning domain.

本研究深入研究了机器学习研究演变的动态景观，利用隐狄利克雷分配方法识别了在机器学习领域中出现的关键主题和基本概念，通过综合分析追踪了这些主题的演化轨迹，并利用 Kullback-Leibler 散度度量方法量化研究贡献的新颖性和多样性，从而了解杰出研究人员的关键作用以及机器学习领域特定学术场所的重要性。

机器学习中的创新和词汇使用模式

Innovation and Word Usage Patterns in Machine Learning

Beyond bibliometrics, there is interest in characterizing the evolution of
the number of ideas in scientific papers. A common approach for investigating
this involves analyzing the titles of publications to detect vocabulary changes
over time. With the notion that phrases, or more specifically keyphrases,
represent concepts, lexical diversity metrics are applied to phrased versions
of the titles. Thus changes in lexical diversity are treated as indicators of
shifts, and possibly expansion, of research. Therefore, optimizing detection of
keyphrases is an important aspect of this process. Rather than just one, we
propose to use multiple phrase detection models with the goal to produce a more
comprehensive set of keyphrases from the source corpora. Another potential
advantage to this approach is that the union and difference of these sets may
provide automated techniques for identifying and omitting non-specific phrases.
We compare the performance of several phrase detection models, analyze the
keyphrase sets output of each, and calculate lexical diversity of corpora
variants incorporating keyphrases from each model, using four common lexical
diversity metrics.

本文研究了利用不同短语检测模型检测科学论文的标题从而找出涵盖科研进展的关键词组，目的在于优化文献词汇多样性的度量与自动化剔除非特定短语。研究结果比较了多个短语检测模型的性能，并分析了每个模型生成的关键词组，使用四个常用的词汇多样性指标计算了每个模型关键词组的文献变量中的词汇多样性。