Identifying interesting assertions from the web

How can we cull the facts we need from the overwhelming mass of information and misinformation that is the Web? The TextRunner extraction engine represents one approach, in which people pose keyword queries or simple questions and TextRunner returns concise answers based on tuples extracted from Web text. Unfortunately, the results returned by engines such as TextRunner include both informative facts (e.g., “the FDA banned ephedra”) and less useful statements (e.g., “the FDA banned products”). This paper therefore investigates filtering TextRunner results to enable people to better focus on interesting assertions. We first develop three distinct models of what assertions are likely to be interesting in response to a query. We then fully operationalize each of these models as a filter over TextRunner results. Finally, we develop a more sophisticated filter that combines the different models using relevance feedback. In a study of human ratings of the interestingness of TextRunn...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Thomas Lin, Oren Etzioni, James Fogarty
• Keywords: CIKM 2009, Database, FDA Banned Ephedra, People Pose Keyword, TextRunner Extraction Engine