Psychological Representations of Multiple Agents in Text and Spoken Discourse

这个项目探讨了话语的心理表征的本质，并将结果应用于一个关于多个主体的文本中“谁知道什么”的计算模型。许多类型的推论和语用效果取决于知道谁可以获得哪些信息，以及谁有共同的立场。文本理解的计算和心理模型在很大程度上忽略了多个代理源对文本信息的表示和使用的影响。我们的目标是：A)研究人们如何处理和表示在构建的和自然发生的文本中所描述或引用的代理人的不同知识状态；B)研究读者对文本的表示如何受到他们对这些文本的观点以及来源的呈现方式的影响；C)将这些经验结果应用于部分自动化系统的构建，用于提取、表示、并在大量自然发生的文本语料库中编码“谁知道什么”——来自《华尔街日报》的新闻故事。这项研究将有助于实现从文本和报告语音中建模和提取信息的实际目标，以及关于人们如何处理和表示多个智能体的文本的理论目标。

This project probes the nature of psychological representations of discourse and applies the results to a computational model of "who knows what" in text about multiple agents. Many kinds of inferences and pragmatic effects depend on knowing who has access to which information, as well as who shares common ground. Computational and psychological models of text understanding have largely ignored the ramifications of multiple agent sources on the representation and use of textual information. Our objectives are: A) to examine how people process and represent the distinct knowledge states of agents depicted or quoted in both constructed and naturally occurring texts, B) to investigate how readers' representations of texts are affected by the perspectives they bring to these texts, as well as by how sources are presented, and C) to apply these empirical results to the construction of a partly-automated system for extracting, representing, and coding "who knows what" in a large corpus of naturally occurring texts - news stories from The Wall Street Journal. This research will contribute to practical goals concerning the modeling and extraction of information from text and reported speech, as well as to theoretical goals concerning how people process and represent texts about multiple agents.