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

许多信息工作者都被不熟悉的文本所淹没。一个挑战是获得大型文本集合的准确概览，例如在‘’‘’‘’‘’通知和评论‘’‘’‘’‘’规则制定中收集的公共评论。目前还没有一种工具能提供这样一个语料库的足够多样化的图像，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而细致的理解。本研究旨在开发一个计算框架，允许从多个综合角度进一步探索这个问题。所有组装的透视图将被整合到一个整体的超文档结构中，该结构在用户指导下动态构建。在这种结构中，分层主题集群将通过观点和论证链接交叉链接，使用两类文本分析引擎：一类用于主题和子主题，另一类用于论证结构。研究团队将设计、开发、构建和系统地测试一个整体的文本探索框架，这是一个支持联邦法规编写者的应用程序，被称为规则编写者工作台。与联邦政府官员有强有力的合作，他们将提供数据并参与用户测试。这三位pi在之前的NSF资助下成功合作了一个相关项目。智力优势：这是计算机科学和政治/社会科学研究之间的可持续合作，植根于具有挑战性和重要的现实世界应用，并通过多年的最终用户研究获得信息。动态的、用户驱动的子主题定义和与语言建模相结合的聚类算法是一组创新但可实现的目标。要开发的框架将以人文学科的修辞学、话语结构和主体性的专业知识为基础。更广泛的影响：规则制定者工作台将允许联邦政府法规制定者使用一套技术工具，对公众对拟议法规的反应进行独立分析，包括近重复检测和聚类，从动态提取的关键字中基于用户的主题选择，意见识别和子主题聚类。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.