DC: Small: Cross-Language Bayesian Models for Web-Scale Text Analysis Using MapReduce

DC：小型：使用 MapReduce 进行 Web 规模文本分析的跨语言贝叶斯模型

• 批准号: 1018625
• 负责人: Jimmy Lin
• 金额: $ 45万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018625&HistoricalAwards=false
• 关键词: DC; Small; Cross; Language; Bayesian

The Web promises unprecedented access to the perspectives of anenormous number of people on a wide range of issues. Turning thatstill untamed cacophony into meaningful insights requires dealing withthe linguistic diversity and scale of the Web. Most current researchfocuses on specialized tasks such as tracking consumer opinions, andvirtually all current research treats the Web as both monolithic andmonolingual, ignoring the variety of languages represented and therich interplay between topics and issues under discussion.This project moves the state of the art forward by focusing on two keychallenges. First, highly-scalable MapReduce algorithms forlinguistic modeling within a Bayesian framework, making use ofvariational inference to achieve a high degree of parallelization onWeb-scale datasets. Second, novel Bayesian models that learnconsistent interpretations of text across languages and a wide rangeof response variables of interest (for example, views on an issue,strength of emotion relative to an event, and focus of attention).The techniques developed in this project will be demonstrated on largecrawls of Web pages and blogs. Potential applications for thesetechnologies include helping a schoolchild learn that people indifferent countries may view some issues very differently, helping apolitician understand how constituents are reacting to proposedlegislation, or helping an intelligence analyst understand how publicopinion is evolving in a hostile country.For further information see the project Web page:http://www.umiacs.umd.edu/~jimmylin/cloud-computing

网络承诺前所未有地接触到大量人对广泛问题的观点。 要将这些尚未驯服的杂音转化为有意义的见解，需要处理语言的多样性和网络的规模。 大多数当前的研究集中在专门的任务上，比如跟踪消费者的意见，而实际上所有当前的研究都把网络看作是单一的和单语言的，忽略了所代表的语言的多样性以及所讨论的主题和问题之间的丰富的相互作用。这个项目通过关注两个关键挑战来推动技术的发展。 首先，高度可扩展的MapReduce算法在贝叶斯框架内进行语言建模，利用变分推理实现Web规模数据集的高度并行化。 第二，新颖的贝叶斯模型，它可以学习跨语言文本的一致解释和广泛的感兴趣的响应变量（例如，对问题的看法，与事件相关的情感强度以及注意力的焦点）。本项目中开发的技术将在大量的网页和博客上展示。 这些技术的潜在应用包括帮助学童了解不同国家的人对某些问题的看法可能非常不同，帮助政治家了解选民对拟议立法的反应，或帮助情报分析师了解敌对国家的民意如何演变。http://www.umiacs.umd.edu/~jimmylin/cloud-computing