Coupled pattern learner: Difference between revisions

Revision as of 03:27, 14 September 2016

Revision as of 14:13, 22 November 2017

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== 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 classifiersclassifiers for many different categories and relations in the presence of an [[ontology]] definingdefining constraints that couple the training of these classifiersclassifiers. 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|url=http://dl.acm.org/citation.cfm?id=1621829.1621830}}</ref><ref name=cpl2010>{{cite journal|last=Carlson|first=Andrew|author2=Justin Betteridge |author3=Richard C. Wang |author4=Estevam R. Hruschka Jr. |author5= Tom M. Mitchell |year=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 Overview==

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 difficultdifficult 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)’.

== CPL Description ==

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=== Relation argument type-checking ===

This is a type checking information used to couple the learning of relations and categories. For example, the arguments of the ‘ceoOf’ relation are declared to be of the categories ‘person’ and ‘company’. CPL does not promote a pair of noun phrases as an instance of a relation unless the two noun phrases are classifiedclassified as belonging to the correct argument types.

=== Algorithm Description ===

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==== Inputs ====

A large [[Text corpus|corpus]] of Part-Of-Speech tagged sentences and an initial ontology with predefinedpredefined categories, relations, mutually exclusive relationships between same-arity predicates, subset relationships between some categories, seed instances for all predicates, and seed patterns for the categories.

==== Candidate extraction ====

CPL findsfinds new candidate instances by using newly promoted patterns to extract the noun phrases that co-occur with those patterns in the text corpus. CPL extracts,

* Category Instances

* Category Patterns

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==== Candidate Filtering ====

Candidate instances and patterns are filteredfiltered to maintain high precision, and to avoid extremely specificspecific patterns. An instance is only considered for assessment if it co-occurs with at least two promoted patterns in the text corpus, and if its co-occurrence count with all promoted patterns is at least three times greater than its co-occurrence count with negative patterns.

==== Candidate Ranking ====

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* {{cite journal|last=Shinyama|first=Yusuke|author2=Satoshi Sekine|year=2006|title=Preemptive information extraction using unrestricted relation discovery|journal=HLT-NAACL}}

* {{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 journal|last=Blum|first=Avrim|author2=Tom Mitchell|year=1998|title=Combining labeled and unlabeled data with co-training|journal=COLT}}

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