Coupled pattern learner: Difference between revisions

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{{Short description|Machine learning algorithm}}

'''Coupled Pattern Learner''' (CPL) is a [[machine learning]] algorithm which couples the [[semi-supervised learning]] of categories and relations to forestall the problem of semantic drift associated with boot-strap learning methods.

== Coupled Pattern Learner ==

[[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 (information science)|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|author2=Justin Betteridge |author3=Estevam R. Hruschka Jr. |author4= Tom M. Mitchell |year=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|isbn=9781932432381 |url=http://dl.acm.org/citation.cfm?id=1621829.1621830}}</ref><ref name=cpl2010>{{cite book|last=Carlson|first=Andrew|author2=Justin Betteridge |author3=Richard C. Wang |author4=Estevam R. Hruschka Jr. |author5= Tom M. Mitchell |title=Proceedings of the third ACM international conference on Web search and data mining |chapter=Coupled semi-supervised learning for information extraction |date=2010 |year=2010|publisher=ACM|___location=NY, USA|pages=101–110|doi=10.1145/1718487.1718501|isbn=9781605588896|doi-access=free}}</ref>

== CPL overview==

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== Meta-Bootstrap Learner ==

Meta-Bootstrap Learner (MBL) was also proposed by the authors of CPL in.<ref name=cpl2010 /> Meta-Bootstrap learner couples the training of multiple extraction techniques with a multi-view constraint, which requires the extractors to agree. It makes addition of coupling constraints on top of existing extraction algorithms, while treating them as black boxes, feasible. MBL assumes that the errors made by different extraction techniques are independent. Following is a quick summary of MBL.

'''Input''': An ontology O, a set of extractors ε

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* {{cite journal|last=Chang|first=Ming-Wei|author2=Lev-Arie Ratinov |author3=Dan Roth |year=2007|title=Guiding semi-supervision with constraint driven learning|journal=ACL}}

* {{cite journal|last=Banko|first=Michele|author2=Michael J. Cafarella |author3=Stephen Soderland |author4=Matt Broadhead |author5=Oren Etzioni |author5-link=Oren Etzioni |year=2007|title=Open information extraction from the web|journal=IJCAI}}

* {{cite journalbook|last=Blum|first=Avrim|author2=Tom Mitchell|yeartitle=1998Proceedings of the eleventh annual conference on Computational learning theory |titlechapter=Combining labeled and unlabeled data with co-training |journalyear=COLT1998|pages=92–100|doi=10.1145/279943.279962|isbn=1581130570|s2cid=207228399 }}

* {{cite journal|last=Riloff|first=Ellen|author2=Rosie Jones|year=1999|title=Learning dictionaries for information extraction by multi-level bootstrapping|journal=AAAI}}

* {{cite journal|last=Rosenfeld|first=Benjamin|author2=Ronen Feldman|year=2007|title=Using corpus statistics on entities to improve semi-supervised relation extraction from the web|journal=ACL}}