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 TextRunner assertions, we show that our approach substantially enhances the quality of TextRunner results. Our best filter raises the fraction of interesting results in the top thirty from 41.6 % to 64.1%.

Large repositories of knowledge can enable more powerful AI systems. Information Extraction (IE) is one approach to building knowledge repositories by extracting knowledge from text. Open IE systems like TextRunner [Banko et al., 2007] are able to extract hundreds of millions of assertions from Web text. However, because of imperfections in extraction technology and the noisy nature of Web text, IE systems return a mix of both useful, informative facts (e.g., "the FDA banned ephedra") and less informative statements (e.g., "the FDA banned products"). This paper investigates using user-contributed knowledge from Wikipedia and from TextRunner website visitors to train classifiers that automatically filter extracted assertions. In a study of human ratings of the interestingness of TextRunner assertions, we show that our approach substantially enhances the quality of results. Our relevance feedback filter raises the fraction of interesting results in the top thirty from 41.6 % to 64.1%. 1

Purpose can be characterized as a central, self-organizing life aim. Central in that when present, purpose is a predominant theme of a person’s identity. Selforganizing in that it provides a framework for systematic behavior patterns in everyday life. As a life aim, a purpose generates continual goals and targets for efforts to be devoted. A purpose provides a bedrock foundation that allows a person to be more resilient to obstacles, stress, and strain. In this paper, we outline a theoretical model of purpose development. Besides outlining various essential ingredients to creating a purpose in life, we describe three broad pathways. The first process is proactive involving effort over time and only resulting in a purpose after gradual refinement and clarification. The second process is reactive involving a transformative life event where a purpose arises and adds clarity to the person's life. The third process is social learning- involving the formation of purpose through observation, imitation, and modeling. Our aim is to stimulate more research on this higher-level construct in the architecture of personality.

We conduct a carefully designed random-assignment experiment to investigate whether negative affect impacts overconfidence. Our result indicates that, compared to neutralaffect, fear and sadness significantly increase overconfidence. All decisions were incentivized, and the result is robust to various specification checks. Further, our result has implications for the role of emotions in economic decision making, in general. Finally, we reconfirm the ubiquity of overconfidence and start to explore its determinants

The Web contains more text than any other source in human history, and continues to expand rapidly. Computer algorithms to process and extract knowledge from Web text have the potential not only to improve Web search, but also to collect a sizable fraction of human knowledge and use it to enable smarter artificial intelligence. To scale to the size and diversity of the Web, many Web text processing algorithms use domain-independent statistical approaches, rather than limiting their processing to any fixed ontologies or sets of domains. While traditional knowledge bases (KBs) had limited coverage of general knowledge, the last few years have seen the rapid rise of new KBs like Freebase and Wikipedia that now cover millions of general interest topics. While these KBs still do not cover the full diversity of the Web, this thesis demonstrates that they are now close enough that there are ways to effectively leverage them in domain-independent Web text processing. It presents and empirically verifies how these KBs can be used to filter uninteresting Web extractions, enhance understanding and usability of both extracted relations and extracted entities, and even power new functionality for Web search. The effective integration of KBs with