CAREER: New Directions in Probabilistic Topic Models

对分析和组织大量电子信息的(半自动)工具的需求日益增长。与之相对应的是，概率主题模型的机器学习研究激增，它可以自动发现大量文档中隐藏的主题结构。一旦明确，这种隐藏的结构有助于浏览、搜索、组织和总结海量信息。这项研究计划将大大建立在当前最先进的主题建模1的基础上。我们将开发主题建模算法，以发现文档流中的趋势。对主题随时间的演变和革命性变化进行建模将是语料库分析员的一项重要新能力，它提供了预测和理解新闻馈送、科学出版物或网络博客等系列集合中变化模式的方法。许多现代语料库，如维基百科，都包含文档之间的重要链接。我们将开发这种相互关联的集合的主题模型，明确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、学术引文、共享作者或统计相关性。捕捉这些连接中的模式，并了解它们与文本的关系，将对各种学术、商业和个人‘推荐’系统具有重要意义。通常，分析师和其他用户在处理语料库时会考虑到特定的问题。为了促进有重点的、个性化的探索，我们将开发发现主题模型的监督方法，这些方法可以预测学术论文、法律摘要、媒体来源和产品规格等在线材料的文档特定变量--特别是相关形式。这个项目解决了主题建模目前的重大局限性，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。我们将以开源软件的形式提供这些工具，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计划中。

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.