AI@CBS

On search keywords with trademarked terms, the brand owner ("focal brand") and other relevant firms compete for consumers. For the focal brand, paid clicks have a direct substitute in the organic links below the paid ad(s). The proximity of this substitute depends on whether competing firms are bidding aggressively to siphon off traffic. We study the returns to focal brands and competitors using large-scale experiments on Bing with data from thousands of brands.

We present evidence on the labor supply of CEOs, and on whether family and professional CEOs differ on this dimension. We do so through a new survey instrument that allows us to codify CEOs' diaries in a detailed and comparable fashion, and to build a bottom-up measure of CEO labor supply. The comparison of 1,114 family and professional CEOs reveals that family CEOs work 9% fewer hours relative to professional CEOs.

In this paper we show how simple text mining and semantic network analysis may be used to (i) improve our theoretical understanding of idea generation, (ii) help people improve the creativity of their ideas. From a theoretical perspective, we contribute to the cognitive idea generation literature by establishing a link between the set of concepts used to form an idea and the creativity of the idea. Each idea contains a subset of the semantic network of concepts related to the topic.

We present an end-to-end text mining methodology for relation extraction of adverse drug reactions (ADRs) from medical forums on the Web. Our methodology is novel in that it combines three major characteristics: (i) an underlying concept of using a head-driven phrase structure grammar (HPSG) based parser; (ii) domain-specific relation patterns, the acquisition of which is done primarily using unsupervised methods applied to a large, unlabeled text corpus; and (iii) automated post-processing algorithms for enhancing the set of extracted relations.

Despite the popularity of prediction, markets among economists, businesses, and policymakers have been slow to adopt them in decision-making. Most studies of prediction markets outside the lab are from public markets with large trading populations. Corporate prediction markets face additional issues, such as thinness, weak incentives, limited entry, and the potential for traders with biases or ulterior motives — raising questions about how well these markets will perform.