lecture 24: relation extraction - computer sciencekc2wc/teaching/nlp16/slides/24...v support...
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Lecture 24: Relation Extraction
Kai-Wei ChangCS @ University of Virginia
Couse webpage: http://kwchang.net/teaching/NLP16
1CS6501-NLP
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Goal
vAcquire structured knowledge from text
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Information extraction
vEntities recognition v Identify name entities: People, Organization,
Location, Times, Dates, etc.vor genes, proteins, diseases, etc.
vRelation extractionvLocation in, employed by, married to
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Example
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Why relation extraction?
v Create structured knowledge bases v Augment structured knowledge basesv Support question answering v The first step for event extraction and storyline
extractionv …
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Relation types (closed domain)
v 17 relations from Automated Content Extraction (ACE)
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Credit:DanJurafsky
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Relation types (closed domain)
vUMLS: Unified Medical Language Systemv 134 entity types, 54 relations
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Relation types (open domain)
vFreebase: thousand relations/million entities
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Wikipedia Infobox
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|undergrad=15,669<refname=facts/>|postgrad=6,316<refname=facts/>|city=[[Charlottesville,Virginia|Charlottesville]]|state=[[Virginia]]|country=U.S.|campus=[[Charlottesville,Virginiametropolitanarea|Small city]]<br/>{{convert|1682|acre|km2}}<br />[[WorldHeritageSite]]
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How to build relation extractors (closed domain)
v Hand-written patternsv Supervised machine learning
vTake each sentence as inputv Identify name entities (mentions) vPerform multi-class classifications
v + constraints or features to model correlations
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How to build relation extractors (open domain)
v Bootstrap learning [Brin 98, …]
v Use seed instances to extract a set of relational patterns
v Unsupervised learningv Cluster sentences based on relational patterns
vDistant supervisionDistant supervision for relation extraction without labeled data [Mintz 09+]
vCombine the above approaches
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v A follow-up approach:Relation Extraction with Matrix Factorization and Universal Schemas [Riedel 13+]
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