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[[Semi-supervised learning]] approaches using a small number of labeled examples with many unlabeled examples are usually unreliable as they produce an internally consistent, but incorrect set of extractions. CPL solves this problem by simultaneously learning classifiers for many different categories and relations in the presence of an [[ontology]] defining constraints that couple the training of these classifiers. It was introduced by Andrew Carlson, Justin Betteridge, Estevam R. Hruschka Jr. and Tom M. Mitchell in 2009. <ref name=cbl2009>{{cite journal|last=Carlson|first=Andrew|coauthors=Justin Betteridge; Estevam R. Hruschka Jr.; Tom M. Mitchell|date=2009|title=Coupling semi-supervised learning of categories and relations|journal=Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing |publisher=Association for Computational Linguistics|___location=Colorado, USA|pages=1–9|url=http://dl.acm.org/citation.cfm?id=1621829.1621830}} </ref> <ref name=cpl2010>{{cite journal|last=Carlson|first=Andrew|coauthors=Justin Betteridge;Richard C. Wang; Estevam R. Hruschka Jr.; Tom M. Mitchell|date=2010|title=Coupled semi-supervised learning for information extraction|journal=Proceedings of the third ACM international conference on Web search and data mining |publisher=ACM|___location=NY, USA|pages=101–110|url=http://dl.acm.org/citation.cfm?doid=1718487.1718501}}</ref>
== CPL
CPL is an approach to [[semi-supervised learning]] that yields more accurate results by coupling the training of many information extractors. Basic idea behind CPL is that semi-supervised training of a single type of extractor such as ‘coach’ is much more difficult than simultaneously training many extractors that cover a variety of inter-related entity and relation types. Using prior knowledge about the relationships between these different entities and relations CPL makes unlabeled data as a useful constraint during training. For e.g., ‘coach(x)’ implies ‘person(x)’ and ‘not sport(x)’.
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