Collaborative Research III-COR: From a Pile of Documents to a Collection of Information: A Framework for Multi-Dimensional Text Analysis

许多信息工作者被不熟悉的文本集合淹没了。一个挑战是获得对大量文本集合的准确概述，例如在规则制定中收集的公众评论。目前还没有一个单一的工具提供关于这样一个语料库的足够多样化的图景，也没有足够的理论来帮助人们探索和形成对这样一个文本集合的深刻和细微的理解。这项研究试图开发一个计算框架，允许从多个、综合的角度进一步探讨这一问题。所有装配好的透视图将被放在一个单一的整体超文档结构中，该结构是在用户指导下动态构建的。在这种结构中，层次化的主题簇将通过意见和论证链接进行交叉链接，使用两类文本分析引擎：一类用于主题和子主题，另一类用于论点结构。研究小组将设计、开发、构建并系统测试一个整体文本探索框架，这是一个支持联邦法规编写者的应用程序，称为规则编写者工作台。与联邦政府官员进行了强有力的合作，他们将提供数据并参与用户测试。在之前的国家科学基金资助下，这三个私人投资机构成功地在一个相关项目上进行了合作。智力价值：这是计算机科学和政治/社会科学研究之间的可持续合作，植根于一个具有挑战性和重要的现实世界应用程序，并以多年的最终用户研究为基础。动态的、用户驱动的副主题定义和聚类算法与语言建模相结合，是一套创新但可以实现的目标。要开发的框架将以人文学科在修辞、语篇结构和主观性方面的专业知识为基础。广泛影响：规则编写者工作台将允许联邦政府法规编写者使用一套技术工具来执行对拟议法规的公众反应的独立分析，包括相似重复检测和聚类、从动态提取的关键字中基于用户的话题选择、观点识别和子话题聚类。这些功能将为联邦评论分析开辟新的途径。

Many information workers are swamped with unfamiliar collections of text. One challenge is to obtain an accurate overview of a large text collection, such as the public comments collected in ''''''''notice and comment'''''''' rulemaking. No single tool currently provides a sufficiently diversified picture of such a corpus, and no adequate theory exists to help people explore and form a deep and nuanced understanding of such a text collection. This research seeks to develop a computational framework that allows further exploration of this problem from multiple, integrated perspectives. All the assembled perspectives will be brought together into a single overall supra-document structure that is dynamically constructed under user guidance. In this structure, hierarchical topic clusters will be cross-linked by opinion and argumentation links, using two classes of text analysis engines: one for topics and subtopics, and the other for argument structures. The research team will design, develop, build, and systematically test an overall text exploration framework, an application to support federal regulation writersone called the Rule-Writers Workbench. There is a strong collaboration with Federal government officials who will provide data and participate in user testing. The three PIs have successfully collaborated on a related project under previous NSF funding. Intellectual Merit: This is a sustainable collaboration between computer science and political/social science research, rooted in a challenging and important real world application and informed by years of end user research. Dynamic, user-driven subtopic definition and clustering algorithms coupled withlanguage modeling are an innovative yet reachable set of goals. The framework to be developed will be grounded in the humanities disciplines'' expertise in rhetoric, discourse structure, and subjectivity.Broader Impacts: The Rule-Writers Workbench will allow federal government regulation writers to employ a suite of technical tools that perform independent analyses of public responses to proposed regulations, including near-duplicate detection and clustering, user-based topic selection from dynamically extracted keywords, opinion identification, and subtopic clustering. These capabilities will open new avenues for federal comment analysis.