Beyond search: statistical topic models for text analysis

ChengXiang Zhai

Abstract: Search is generally a means to the end of finishing a task. While the current search engines are useful to users for finding relevant information, they offer little help to users for further digesting and analyzing the overwhelming found information needed for finishing a complex task. In this talk, I will discuss how statistical topic models can be used to help users analyze and digest the found relevant information and turn search results into actionable knowledge needed to complete a task. I will present several general statistical topic models for extracting and analyzing topics and their patterns in text, and show sample applications of such models in tasks such as opinion integration, comparative summarization, contextual topic trend analysis, and event impact analysis. The talk will conclude with a discussion of novel challenges raised in extending a search engine to an analysis engine that can go beyond search to provide more complete support for users to finish their tasks.