ABSTRACT

This paper discusses Story Analyzer, which uses a natural language processing (NLP) library and sophisticated data visualization libraries to produce dashboards of interrelated and user-responsive visualizations depicting actors and their interactions in a textual narrative, along with locations, times, and other contexts. Story Analyzer performs information extraction using Stanford’s CoreNLP’s NLP services including sentence recognition, tokenizing, parts-of-speech identification, dependency parsing, named entity recognition, coreference resolution, and temporal tagging. Visualization is done through D3 and scalable vector graphics (SVG) which provide powerful control over gelements and shapes in browser-based user interfaces. Google Charts and Maps are also used for visualizations. Development using NLP for unstructured textual data involves challenges, limitations, and ambiguities that distinguishes it from applications using structured data. Therefore, the paper also discusses issues and limitations inherent with using NLP libraries, and presents workarounds when applied to story analysis. Story Analyzer is applied to a contemporary news article regarding data privacy issues.

摘要

本文讨论了 Story Analyzer，它使用自然语言处理 (NLP) 库和复杂的数据可视化库来生成相互关联和用户响应的可视化仪表板，以文本叙述方式描述演员及其交互，以及位置、时间和其他上下文. Story Analyzer 使用斯坦福大学的 CoreNLP 的 NLP 服务执行信息提取，包括句子识别、标记化、词性识别、依赖解析、命名实体识别、共指解析和时间标记。可视化是通过 D3 和可缩放矢量图形 (SVG) 完成的，它们在基于浏览器的用户界面中提供对 gelements 和形状的强大控制。谷歌图表和地图也用于可视化。将 NLP 用于非结构化文本数据的开发涉及挑战、限制和模糊性，这些都将其与使用结构化数据的应用程序区分开来。因此，本文还讨论了使用 NLP 库所固有的问题和局限性，并提出了应用于故事分析时的变通方法。Story Analyzer 应用于有关数据隐私问题的当代新闻文章。