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Abstract The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KNOW-ITALL's novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KNOW-ITALL extracted over 50,000 class instances, but suggested a challenge: How can we improve KNOWITALL's recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall (e.g., "chemist" and "biologist" are identified as sub-classes of "scientist"). List Extraction locates lists of class instances, learns a "wrapper" for each list, and extracts elements of each list. Since each method bootstraps from KNOWITALL's domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KNOWITALL a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.

. These models are useful for theory development and testing as well as for the identification of possible points of intervention in applied work. Mediation is equally of interest to experimental psychologists as it is to those who study naturally occurring processes through nonexperimental studies. For example

This paper considers causal inference and sample selection bias in non-experimental settings in which: (i) few units in the non-experimental comparison group are comparable to the treatment units; and (ii) selecting a subset of comparison units similar to the treatment units is difficult because

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This paper presents studies of the coordination of volun-tary human arm movements. A mathematical model is for-mulated which is shown to predict both the qualitative fea-tures and the quantitative details observed experimentally in planar, multijoint arm movements. Coordination is modeled

complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper

approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance

This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and K-means. (We used both a “standard” K-means algorithm and a “bisecting ” K-means algorithm.) Our results indicate that the bisecting K-means technique

this paper, Gnutella is no exception to this finding, and an experimental study of its user patterns shows indeed that free riding is the norm rather than the exception. If distributed systems such as Gnutella rely on voluntary cooperation, rampant free riding may eventually render them useless