2010 TowardAnArchForNeverEndLangLearn

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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 a set of design principles for such an agent, describe a partial implementation of such a system that has already learned to extract a knowledge base containing over 242,000 beliefs with an estimated precision of 74% after running for 67 days, and discuss lessons learned from this preliminary attempt to build a never-ending learning agent.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2010 TowardAnArchForNeverEndLangLearnAndrew Carlson
Justin Betteridge
Bryan Kisiel
Burr Settles
Estevam R. Hruschka
Tom M. Mitchell
Toward an Architecture for Never-Ending Language LearningAAAI 2010http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/18792010