Decomposing unaccusativity: a statistical modelling approach

While the two types of intransitive verbs, i.e. unergative and unaccusative, are hypothesised to be syntactically represented, many have proposed a semantic account where abstract properties related to agentivity and telicity, often conceptualised as binary properties, determine the classification. Here we explore the extent to which graded, embodied features rooted in neurobiological systems contribute to the distinction, representing verb meanings as continuous human ratings over various experiential dimensions. Unlike prior studies that classified verbs based on categorical intuition, we assessed the degree of unaccusativity by acceptability of the prenominal past participle construction, one of the unaccusativity diagnostics. Five models were constructed to explain these data: categorical syntactic/semantic, feature-based event-semantic, experiential, and distributional models. The experiential model best explained the diagnostic test data, suggesting that the unaccusative/unergative distinction may be an emergent phenomenon related to differences in underlying experiential content. The experiential model’s advantages, including interpretability and scalability, are also discussed.

尽管学界已提出两类不及物动词——非作格动词（unergative）与非宾格动词（unaccusative）——具备句法表征的假设，但诸多研究均主张采用语义解释框架：即通常被概念化为二元属性的、与施事性和终结性相关的抽象属性，决定了动词的分类。本研究旨在探究植根于神经生物学系统的等级化具身特征，在二者区分中所起到的作用，并将动词意义表征为基于各类经验维度的连续人类评分。与此前基于范畴化直觉对动词进行分类的研究不同，本研究通过前置过去分词结构（非宾格性诊断手段之一）的可接受度，评估动词的非宾格性程度。本研究构建了五类模型以解释上述数据：范畴化句法/语义模型、基于特征的事件语义模型、经验模型与分布语义模型。结果显示，经验模型对诊断测试数据的解释力最优，这表明非宾格与非作格动词的区分，或许是一类源于底层经验内容差异的突现现象。本研究同时讨论了经验模型的优势，包括可解释性与可扩展性。