Psychological Representations of Multiple Agents in Text and Spoken Discourse

这个项目探索了话语的心理表征的性质，并将结果应用于文本中关于多个代理人的“谁知道什么”的计算模型。许多类型的推论和实际效果取决于了解谁有权获得哪些信息，以及谁有共同点。文本理解的计算和心理模型在很大程度上忽略了多个智能体来源对文本信息的表征和使用的影响。我们的目标是：A)检查人们如何处理和表示在构建和自然发生的文本中描述或引用的代理人的不同知识状态，B)调查读者对文本的表示如何受到他们对这些文本的视角以及来源的呈现方式的影响，以及C)将这些经验结果应用于构建一个部分自动化的系统，用于在自然发生的大型文本语料库--来自《华尔街日报》的新闻故事--中提取、表示和编码“谁知道什么”。本研究将有助于实现从文本和报告的语音中建模和提取信息的实践目标，以及关于人们如何处理和表示关于多个代理人的文本的理论目标。

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.