Andrew Carlson

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We consider here the problem of building a never-ending language learner; that is, an intelligent computer agent that runs forever and that each day must (1) extract, or read, information from the web to populate a growing structured knowledge base, and (2) learn to perform this task better than on the previous day. In particular, we propose an approach and(More)
The question of how the human brain represents conceptual knowledge has been debated in many scientific fields. Brain imaging studies have shown that different spatial patterns of neural activation are associated with thinking about different semantic categories of pictures and words (for example, tools, buildings, and animals). We present a computational(More)
We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled(More)
We consider semi-supervised learning of information extraction methods, especially for extracting instances of noun categories (e.g., ‘athlete,’ ‘team’) and relations (e.g., ‘playsForTeam(athlete,team)’). Semisupervised approaches using a small number of labeled examples together with many unlabeled examples are often unreliable as they frequently produce(More)
Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a neverending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by(More)
Process-induced variations and sub-threshold leakage in bulk-Si technology limit the scaling of SRAM into sub-32 nm nodes. New device architectures are being considered to improve control and reduce short channel effects. Among the likely candidates, FinFETs are the most attractive option because of their good scalability and possibilities for further SRAM(More)
We report research toward a never-ending language learning system, focusing on a first implementation which learns to classify occurrences of noun phrases according to lexical categories such as “city” and “university.” Our experiments suggest that the accuracy of classifiers produced by semi-supervised learning can be improved by coupling the learning of(More)
Reversible control of adhesion is an important feature of many desired, existing, and potential systems, including climbing robots, medical tapes, and stamps for transfer printing. We present experimental and theoretical studies of pressure modulated adhesion between flat, stiff objects and elastomeric surfaces with sharp features of surface relief in(More)