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

网络提供了前所未有的机会，让大量的人就各种各样的问题发表看法。要将这些尚未驯服的杂音转变为有意义的见解，需要应对网络的语言多样性和规模。目前的大多数研究都集中在跟踪消费者意见等专门任务上，而几乎所有的当前研究都将网络视为单一的和单一的语言，而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相互作用。该项目通过关注两个关键挑战来推动最新技术的发展。首先，在贝叶斯框架内实现了高度可扩展的语言建模算法，利用变分推理在Web规模的数据集上实现了高度的并行化。第二，新的贝叶斯模型，它可以学习跨语言对文本的一致解释和广泛的感兴趣的反应变量(例如，对一个问题的看法，相对于一个事件的情绪强度，以及注意力的焦点)。这个项目中开发的技术将在大量的网页和博客上进行演示。这些技术的潜在应用包括帮助学生了解不同国家的人可能对一些问题有非常不同的看法，帮助非政治主义者了解选民对拟议的立法的反应，或者帮助情报分析员了解敌对国家的公众舆论是如何演变的。有关更多信息，请参阅项目Web page:http://www.umiacs.umd.edu/~jimmylin/cloud-computing

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