CAREER: New Directions in Probabilistic Topic Models

越来越需要（半）自动化的工具来分析和组织大量的电子信息。作为回应，人们对概率主题模型的机器学习进行了大量研究，这种模型可以自动发现大量文档中隐藏的主题结构。一旦明确，这种隐藏的结构便于浏览、搜索、组织和总结大量信息。本研究计划将显著建立在当前主题建模的最新技术基础上。我们将开发主题建模算法来发现文档流中的趋势。随着时间的推移，对主题的进化和革命性变化进行建模将是语料库分析师的一项重要的新能力，它提供了预测和理解系列集合（如新闻提要、科学出版物或网络博客）中变化模式的方法。许多现代语料库，如维基百科，包含文档之间的重要链接。我们将为这种相互关联的集合开发主题模型，以显式地表示和概括文档间和/或主题间的关系。这种关系可能是超链接、学术引用、共享作者或统计相关性。捕捉这些联系中的模式，并理解它们与文本的关系，将对各种各样的学术、商业和个人“推荐”系统产生重要影响。通常，分析人员和其他用户会带着特定的问题来处理语料库。为了促进集中的、个性化的探索，我们将开发有监督的方法来发现预测文档特定变量的主题模型——特别是相关形式——在线材料，如学术论文、法律摘要、媒体来源和产品规格。该项目解决了当前主题建模的重大限制，并将为理解和组织现代信息库提供实用的新研究和教育工具。我们将把这些工具作为开源软件提供，以支持和鼓励它们应用于现实世界的问题，我们将把我们的研究成果纳入正在进行的教育和推广项目中。

There is a growing need for (semi-)automated tools to analyze and organize large collections of electronic information. In response, there is a surge of research on machine learning of probabilistic topic models, which automatically discover the hidden thematic structure in a large collection of documents. Once made explicit, this hidden structure facilitates browsing, searching, organizing, and summarizing vast amounts of information.This research program will significantly build on the current state-of-the-art in topic modeling.1. We will develop topic modeling algorithms that discover trends in document streams. Modeling evolutionary and revolutionary change of topics over time will be an important new capability for corpora analysts, providing methods of forecasting and understanding the changing patterns in serial collections such as news feeds, scientific publications, or web blogs.2. Many modern corpora, such as Wikipedia, contain important links between the documents. We will develop topic models of such interconnected collections that explicitly represent and generalize inter-document and/or inter-topic relationships. Such relationships may be hyper-links, scholarly citation, shared authorship, or statistical correlations. Capturing the patterns in these connections, and understanding their relationship to the texts, will have important implications for a great variety of scholarly, commercial, and personal 'recommender' systems.3. Very often, analysts and other users approach a corpora with particular questions in mind. To facilitate focused, personalized exploration, we will develop supervised methods for discovering topic models that predict document-specific variables -- notably forms of relevance -- of online material such as scholarly papers, legal briefs, media sources, and product specifications.This project addresses significant current limitations of topic modeling, and will provide practical new research and education tools for understanding and organizing modern repositories of information. We will make these tools available as open-source software to support and encourage their application to real-world problems, and we will fold the results of our research into ongoing education and outreach programs.