CAREER: New Directions in Probabilistic Topic Models

职业：概率主题模型的新方向

• 批准号: 0745520
• 负责人: David Blei
• 金额: $ 54.99万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0745520&HistoricalAwards=false
• 关键词: CAREER; New; Directions; Probabilistic; Topic

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.

（半）自动化工具的需求日益增长，以分析和组织大量电子信息收集。 作为回应，对概率主题模型的机器学习有了一系列的研究，它们会在大量文档集合中自动发现隐藏的主题结构。 一旦明确说明，这种隐藏的结构便有助于浏览，搜索，组织和汇总大量信息。此研究计划将大大建立在主题建模中的最新目前。1。我们将开发主题建模算法，以发现文档流中的趋势。随着时间的推移，建模主题的进化和革命性变化将是Corpora分析师的重要新能力，提供了预测和理解串行收藏中不断变化的模式的方法，例如新闻提要，科学出版物或Web Blogs.2。许多现代语料库，例如Wikipedia，都包含文档之间的重要联系。 我们将开发此类相互联系的集合的主题模型，这些模型明确表示和概括了文档间和/或主题间关系。这种关系可能是超链接，学术引用，共享作者身份或统计相关性。 捕获这些联系中的模式，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。3。经常，分析师和其他用户会牢记特定问题的语料库。为了促进集中，个性化的探索，我们将开发有监督的方法来发现主题模型，以预测在线材料（例如学术论文，法律简介，媒体来源和产品规格）等在线材料的特定文档变量，尤其是相关性的形式。该项目解决了当前的主题建模的重大局限性，并将提供实用的新研究和教育工具，以理解现代化的现代知识库。我们将将这些工具作为开源软件提供，以支持和鼓励其在现实世界中的应用，并将研究结果折叠到正在进行的教育和外展计划中。