Human-computer interaction: toward the year 2000
Human-computer interaction: toward the year 2000
On the MSE robustness of batching estimators
Proceedings of the 33nd conference on Winter simulation
ECML '93 Proceedings of the European Conference on Machine Learning
Collective knowledge systems: Where the Social Web meets the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Amplifying community content creation with mixed initiative information extraction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Populating the Semantic Web by Macro-reading Internet Text
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Coupled semi-supervised learning for information extraction
Proceedings of the third ACM international conference on Web search and data mining
Hierarchical reinforcement learning for adaptive text generation
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Prophet -- A Link-Predictor to Learn New Rules on NELL
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Random walk inference and learning in a large scale knowledge base
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Discovering relations between noun categories
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Autonomously reviewing and validating the knowledge base of a never-ending learning system
Proceedings of the 22nd international conference on World Wide Web companion
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The recent growth of virtual communities, social web and information sharing gives to information retrieval and machine learning systems a source of information referred as the "wisdom of crowds". In this work we show that this information could be used not only as a source of knowledge but as a way to bring intelligent systems closer to users by using their opinion as part of the knowledge acquisition/validation allowing self-supervision. For that we have implemented a validation system for the NELL (Never-Ending Language Learner) system using the question answering platform given by the Yahoo! Answers web community. Moreover, we focus in this paper, in the validation of first order rules induced by NELL using its Rule Learning (RL) algorithm. This paper presents the main motivations for using a QA forum instead of other web-based validation sources; describes the proposed approach with a "Macro QA"-based component named SS-Crowd (self-supervisor agent based on the wisdom of crowds) and brings and discusses the obtained results and how they can impact in a never-ending learning system like NELL in which self-supervision plays a crucial role.