NDLI: Collective intelligence as a source for machine learning self-supervision

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Collective intelligence as a source for machine learning self-supervision

Content Provider	ACM Digital Library
Author	Pedro, Saulo D. S. Hruschka, Estevam R.
Abstract	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.
Starting Page	1
Ending Page	9
Page Count	9
File Format	PDF
ISBN	9781450311892
DOI	10.1145/2189736.2189744
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Knowledge validation Machine learning Question answering Self-supervision
Content Type	Text
Resource Type	Article

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