III: Small: Collaborative Research: Automatically Generating Contextually-Relevant Visualizations

信息可视化，如图表和地图，可以通过添加上下文，帮助读者理解复杂的事实，帮助决策，并使信息更加难忘，从而大大增强新闻文章。不幸的是，创建好的新闻可视化是一项困难且劳动密集型的任务，涉及许多复杂的决策。设计师必须识别与文章相关的数据、清理数据、生成可视化（本身就是一个复杂的过程），并提供注释来连接文章和可视化。虽然已经制定了一些设计指南，但许多决策都是基于设计师的直觉，这一过程无法扩展到每天发布的数千篇新闻文章。该项目旨在构建智能工具，以帮助设计师更快地创建好的新闻可视化，并开发自动生成新闻可视化的系统。该研究项目将提高公民对新闻中复杂信息的理解，并提高数字，图形和地理素养。此外，该研究将为新的工作类别提供支持（例如，数据科学家、计算记者、数据分析师等）和现有公司（例如，在线媒体、搜索引擎等）在向新的交互式平台发展的过程中。研究成果将被整合到一套广泛的广泛访问的教育材料的各种课程（可视化，空间计算和文本分析），并将作为研究和实践培训的本科生，研究生和专业人士。提供一个可扩展的解决方案来自动生成上下文相关的可视化需要对设计过程的理解和编码。具体来说，这个项目的目标是（a）识别可视化设计者的决策过程，（B）创建自动化组件来操作这些决策，包括文本处理，搜索各种异构数据源和数据集（例如，人口普查数据、股票市场数据、政府宏观经济数据），以及自动可视化构造和注释，以及（c）基于来自信息检索和信息可视化的公知的定量度量（诸如相关性、表现力和有效性）对可视化进行排序。通过使用自然语言处理从文章的文本中提取关键比较，并使用现有的可视化-文章对作为评估语料库，该系统将确保找到相关数据集，并且所选择的视觉形式保留并增强文章中传达的信息。例如，该系统将自动创建专题地图，用于美国人口变化的地理空间比较，以及用于公司财务业绩纵向比较的时间序列。虽然这项工作的重点在新闻领域，研究可以扩展到其他应用领域，包括教科书，内部公司报告，更普遍的是，任何文本，隐含或明确对应的定量数据。更多的信息，包括源代码、演示、论文和数据集，可以在项目主页（http：//www.example.com）上找到。txt2vis.cond.org

Information visualizations, such as charts and maps, can greatly enhance news articles by adding context, helping the reader understand complex facts, aiding in decision making, and making information more memorable. Unfortunately, creating good news visualizations is a difficult and labor-intensive task that involves numerous complex decisions. A designer must identify data relevant to the article, clean the data, generate the visualization (a complex process on its own), and provide annotations to connect the article and visualization. While some design guidelines have been developed, many decisions are based on designer intuition, a process that is not scalable to the thousands of news articles that are published every day. This project seeks to build intelligent tools to help designers more quickly create good news visualizations and to develop systems that generate news visualizations autonomously. This research project will enhance citizen understanding of complex information in the news and improve numerical, graphical, and geography literacy. Additionally, the research will provide support for new job categories (e.g., data scientists, computational journalists, data analysts, etc.) and existing companies (e.g., online media, search engines, etc.) in their evolution to new interactive platforms. The research results will be integrated into a broad set of widely accessible educational materials for a variety of courses (visualization, spatial computing, and text analysis) and will serve as research and practical training for undergraduates, graduates, and professionals. Providing a scalable solution to automatically generating contextually-relevant visualizations requires the understanding and encoding of the design process. Specifically, the goals of this project are (a) identifying the decision process of visualization designers, (b) creating automated components that operationalize these decisions including text processing, searching through a wide range of heterogeneous data sources and datasets (e.g. census data, stock market data, government macroeconomic data), and automatic visualization construction and annotation, and (c) ranking of the visualizations based on well-known quantitative metrics from information retrieval and information visualization such as relevance, expressiveness, and effectiveness. By extracting key comparisons from an article's text through the use of natural language processing and using existing visualization-article pairs as an evaluation corpus, the system will ensure that relevant datasets are found and that the selected visual forms preserve and enhance the information conveyed in the article. For example, the system will automatically create thematic maps for geospatial comparisons of population change in the U.S. and time series for longitudinal comparisons of company financial results. Although the focus of this work in on the news domain, the research can be extended to other application areas including textbooks, internal company reports, and more generally, to any texts that implicitly or explicitly correspond to quantitative data. Further information, including source code, demos, papers, and datasets, are available at the project homepage (http://txt2vis.cond.org).