This article provides an overview of graded and probabilistic approaches in semantics and pragmatics. These approaches share a common set of core research goals: ( a) a concern with phenomena that are best described as graded, including a vast lexicon of words whose meanings adapt flexibly to the contexts in which they are used, as well as reasoning under uncertainty about interlocutors, their goals, and their strategies; ( b) the need to show that representations are learnable, i.e., that a listener can learn semantic representations and pragmatic reasoning from data; ( c) an emphasis on empirical evaluation against experimental data or corpus data at scale; and ( d) scaling up to the full size of the lexicon. The methods used are sometimes explicitly probabilistic and sometimes not. Previously, there were assumed to be clear boundaries among probabilistic frameworks, classifiers in machine learning, and distributional approaches, but these boundaries have been blurred. Frameworks in semantics and pragmatics use all three of these, sometimes in combination, to address the four core research questions above.

中文翻译：

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语义学和语用学的概率转向

本文概述了语义和语用学中的分级和概率方法。这些方法有一套共同的核心研究目标：（a）关注最能描述为分级的现象，包括大量词汇，其含义灵活地适应使用它们的上下文，以及在不确定性下的推理关于对话者、他们的目标和策略；(b) 需要证明表示是可学习的，即听者可以从数据中学习语义表示和语用推理；(c) 强调对大规模实验数据或语料库数据的经验评估；(d) 扩大到词典的全尺寸。使用的方法有时是明确的概率，有时不是。之前，假设概率框架、机器学习中的分类器和分布方法之间有明确的界限，但这些界限已经模糊了。语义和语用学框架使用所有这三个，有时结合使用，来解决上述四个核心研究问题。