This work addresses some questions about language processing: what does it mean that natural language sentences are semantically complex? What semantic features can determine different degrees of difficulty for human comprehenders? Our goal is to introduce a framework for argument semantic complexity, in which the processing difficulty depends on the typicality of the arguments in the sentence, that is, their degree of compatibility with the selectional constraints of the predicate. We postulate that complexity depends on the difficulty of building a semantic representation of the event or the situation conveyed by a sentence. This representation can be either retrieved directly from the semantic memory or built dynamically by solving the constraints included in the stored representations. To support this postulation, we built a Distributional Semantic Model to compute a compositional cost function for the sentence unification process. Our evaluation on psycholinguistic datasets reveals that the model is able to account for semantic phenomena such as the context-sensitive update of argument expectations and the processing of logical metonymies.

这项工作解决了一些有关语言处理的问题：自然语言句子在语义上是什么意思？哪些语义特征可以确定人类理解的不同难度？我们的目标是为论点语义复杂性引入一个框架，其中处理难度取决于句子中论点的典型性，即其与谓词选择约束的兼容程度。我们假定复杂性取决于构建事件或句子传达的情况的语义表示的难度。可以直接从语义内存中检索此表示形式，也可以通过解决存储的表示形式中包含的约束来动态构建此表示形式。为了支持这种假设，我们建立了一个分布语义模型来计算句子统一过程的合成成本函数。我们对心理语言学数据集的评估表明，该模型能够解决语义现象，例如对参数期望的上下文相关更新和对逻辑转喻的处理。