Ambiguity is a natural language phenomenon occurring at different levels of syntax, semantics, and pragmatics. It is widely studied; in Psycholinguistics, for instance, we have a variety of competing studies for the human disambiguation processes. These studies are empirical and based on eyetracking measurements. Here we take first steps towards formalizing these processes for semantic ambiguities where we identified the presence of two features: (1) joint plausibility degrees of different possible interpretations, (2) causal structures according to which certain words play a more substantial role in the processes. The novel sheaf-theoretic model of definite causality developed by Gogioso and Pinzani in QPL 2021 offers tools to model and reason about these features. We applied this theory to a dataset of ambiguous phrases extracted from Psycholinguistics literature and their human plausibility judgements collected by us using the Amazon Mechanical Turk engine. We measured the causal fractions of different disambiguation orders within the phrases and discovered two prominent orders: from subject to verb in the subject-verb and from object to verb in the verb object phrases. We also found evidence for delay in the disambiguation of polysemous vs homonymous verbs, again compatible with Psycholinguistic findings.
翻译:模糊性是一种自然的语言现象,存在于不同层次的语法、语义和务实上。它得到了广泛研究;例如,在精神语言学中,我们对于人类脱节过程有各种相互竞争的研究。这些研究是经验性的,以眼跟踪测量为基础。我们在这里采取初步步骤,正式确定这些语义模糊性的过程,我们发现存在两种特征:(1) 各种可能解释的共同合理度,(2) 因果关系结构,根据这种结构,某些词在过程中发挥更重大的作用。Gogioso和Pinzani在 QPL 2021 中开发的确定因果关系的新颖理论模型为这些特征的模型和理由提供了工具。我们用这一理论对从精神语言学文献中提取的模棱两可的词组数据及其由我们用亚马逊机械土耳其引擎收集的人类的可理解性判断值。我们测量了该词组中不同模糊性命令的因果关系部分,并发现了两个突出的顺序:从主题的动词到主题的动词,以及用QPLPL 201的物体来模拟和解释性结果。我们又用一个比多变的软性符号的比变相比。