III: Small: Multi-field Hierarchical Discovery and Tracking (mf-HDT) of Emerging Topics

III：小型：新兴主题的多领域分层发现和跟踪（mf-HDT）

• 批准号: 1216282
• 负责人: Yiming Yang
• 金额: $ 49.92万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216282&HistoricalAwards=false
• 关键词: III; Small; Multi; field; Hierarchical

The goal of this project addresses the open challenge of Multi-field Hierarchical Discovery and Tracking (mf-HDT) of emerging topics at different granularity levels based on combined evidence in heterogeneous data. The technical approaches consist of a new Bayesian framework with powerful inference algorithms, namely the multi-field Hierarchical Correlated Topic Modeling, for discovering multi-field hierarchies of latent topics, capturing inter-topic and cross-hierarchy correlations, and enabling query-driven threading of topics over a Markov chain of hierarchies. These technical innovations and capabilities go beyond existing Topic Detection and Tracking (TDT) methods and graphical models used to represent relationships between topics, citations, etc. Significant improvements are expected in both effectiveness and scalability over the existing methods, especially in terms of detecting newly emerging topics and tacking time-sensitive impact. The proposed approach will be evaluated on a four large datasets of scientific literature data in a broad range (Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance and Statistics) as well as news stories, with human-produced queries and relevance judgments and human-assigned topic labels to support task-oriented evaluations. Productivity of researchers, educational practitioners and students, government agencies supporting research and industries highly depends on the availability of up-to-date big pictures about scientific emergence and co-emergence within and across many fields, along with evidence of the impact of new technologies, and research or development funding. The proposed techniques, if successful, will provide principled and effective solutions with a broad future impact in the applications above and beyond. Web site (http://nyc.lti.cs.cmu.edu/mfhdt/) will provide access to open-source software, of datasets, results and publication in order to enable comparative evaluations and further studies by related research communities. The students involved in the project benefit from direct experience with using and evaluating cutting-edge IT technologies in real-world applications. This is complementary to classroom teaching where the students can observe first-hand the direct implication of choosing various strategies for categorization, active learning and distributed computing.

该项目的目标是解决基于异构数据中的组合证据的不同粒度级别的新兴主题的多字段分层发现和跟踪（mf-HDT）的开放性挑战。该技术方法包括一个新的贝叶斯框架与强大的推理算法，即多领域层次相关主题建模，用于发现多领域层次的潜在主题，捕捉主题间和跨层次的相关性，并使查询驱动的线程的主题层次结构的马尔可夫链。这些技术创新和能力超越了现有的主题检测和跟踪（TDT）的方法和图形模型，用于表示主题之间的关系，引文等显着的改进，预计在有效性和可扩展性超过现有的方法，特别是在检测新出现的主题和跟踪时间敏感的影响。 该方法将在四个大的科学文献数据集（物理学，数学，计算机科学，定量生物学，定量金融和统计学）以及新闻故事上进行评估，并使用人工查询和相关性判断以及人为分配的主题标签来支持面向任务的评估。研究人员、教育从业者和学生、支持研究和产业的政府机构的生产力高度依赖于关于科学涌现和许多领域内和跨领域共同涌现的最新大图景的可用性，沿着新技术影响的证据，以及研究或开发资金。所提出的技术，如果成功的话，将提供原则性和有效的解决方案，具有广泛的未来影响的应用程序以上和超越。网站（http：//nyc.lti.cs.cmu.edu/mfhdt/）将提供开放源码软件、数据集、结果和出版物，以便有关研究界能够进行比较评价和进一步研究。参与该项目的学生受益于在现实世界的应用中使用和评估尖端IT技术的直接经验。这是课堂教学的补充，学生可以直接观察到选择分类，主动学习和分布式计算的各种策略的直接含义。