Modelling the Information Density of Event Sequences in Texts (A03)

A3项目旨在从数据中收集关于典型事件序列的形式化知识-脚本知识，并利用这些知识来改进自然语言处理的算法，以及我们对人类交流中语言编码选择和解释的理解。该项目将开发从未加注释的文本中学习具有广泛覆盖面的脚本的方法，并使用关于其前提条件和影响的信息来扩展脚本事件的表示法，以跟踪事件之间的因果关系。这些更深更广的脚本模型将应用于各种自然语言处理任务，并用于建模语用解释；我们将使用和扩展Rational Speech Act(RSA)模型作为建模语用推理的框架，并探索RSA模型如何与SFB中使用的现有概念相关，特别是UID假设。

Project A3 aims at collecting formalized knowledge about prototypical sequences of events – script knowledge – from data, and using it to improve algorithms for natural language processing and our understanding of linguistic encoding choice and interpretation in human communication. The project will develop methods for learning scripts with wide coverage from unannotated texts and extend the representations of script events with information about their preconditions and effects to keep track of causal connections between events. These deeper and wider-coverage script models will be applied to various natural language processing tasks, and used to model pragmatic interpretations; we will use and extend the Rational Speech Act (RSA) model as a framework for modelling pragmatic inferences and explore how the RSA model can be related to existing notions used in the SFB, specifically the UID hypothesis.