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.

对（半）自动化工具来分析和组织大量电子信息的需求日益增长。 因此，关于概率主题模型的机器学习的研究激增，它可以自动发现大量文档中隐藏的主题结构。 一旦明确，这种隐藏的结构将有助于浏览、搜索、组织和总结大量信息。该研究计划将显着建立在主题建模的当前最先进的基础上。1．我们将开发主题建模算法来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成为语料库分析师的一项重要新功能，提供预测和理解新闻源、科学出版物或网络博客等系列集合中变化模式的方法。2．许多现代语料库，例如维基百科，都包含文档之间的重要链接。 我们将开发此类互连集合的主题模型，以明确表示和概括文档间和/或主题间关系。这种关系可能是超链接、学术引用、共同作者身份或统计相关性。 捕捉这些联系中的模式，并理解它们与文本的关系，将对各种学术、商业和个人“推荐”系统产生重要影响。3．很多时候，分析师和其他用户在使用语料库时都会考虑到特定的问题。为了促进集中、个性化的探索，我们将开发监督方法来发现主题模型，预测在线材料（例如学术论文、法律简报、媒体来源和产品规格）的文档特定变量（尤其是相关形式）。该项目解决了当前主题建模的重大局限性，并将为理解和组织现代信息存储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，并且我们将把我们的研究成果纳入持续的教育和推广计划中。