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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)
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)
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 never-ending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by(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(More)
A key question regarding the future of the semantic web is " how will we acquire structured information to populate the semantic web on a vast scale? " One approach is to enter this information manually. A second approach is to take advantage of pre-existing databases, and to develop common ontologies, publishing standards, and reward systems to make this(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 consider the problem of extracting structured records from semi-structured web pages with no human supervision required for each target web site. Previous work on this problem has either required significant human effort for each target site or used brittle heuristics to identify semantic data types. Our method only requires annotation for a few pages(More)
While gazetteers can be used to perform named entity recognition through lookup-based methods, ambiguity and incomplete gazetteers lead to relatively low recall. A sequence model which uses more general features can achieve higher recall while maintaining reasonable precision , but typically requires expensive annotated training data. To circumvent the need(More)