1. Lecture 24: Relation Extraction
Kai-Wei Chang
University of Virginia
Couse webpage:
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2. Goal
Acquire structured knowledge from text
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3. Information extraction
Entities recognition
Identify name entities: People, Organization, Location, Times, Dates, etc.
or genes, proteins, diseases, etc.
Relation extraction
Location in, employed by, married to
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4. Example
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5. Why relation extraction?
Create structured knowledge bases
Augment structured knowledge bases
Support question answering
The first step for event extraction and storyline extraction …
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6. Relation types (closed domain)
17 relations from Automated Content Extraction (ACE)
Credit: Dan Jurafsky
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7. Relation types (closed domain)
UMLS: Unified Medical Language System
134 entity types, 54 relations
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8. Relation types (open domain)
Freebase: thousand relations/million entities
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9. Wikipedia Infobox
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10. |undergrad = 15,669<ref name=facts/>
|postgrad = 6,316<ref name=facts/>
|city = [[Charlottesville, Virginia|Charlottesville]]|state = [[Virginia]]|country = U.S.
|campus = [[Charlottesville, Virginia metropolitan area|Small city]]<br />{{convert|1682|acre|km2}}<br />[[World Heritage Site]]
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11. How to build relation extractors (closed domain)
Hand-written patterns
Supervised machine learning
Take each sentence as input
Identify name entities (mentions)
Perform multi-class classifications
+ constraints or features to model correlations
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12. CS6501-NLP

13. How to build relation extractors (open domain)
Bootstrap learning [Brin 98, …]
Use seed instances to extract a set of relational patterns
Unsupervised learning
Cluster sentences based on relational patterns
Distant supervision Distant supervision for relation extraction without labeled data [Mintz 09+]
Combine the above approaches
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14. A follow-up approach: Relation Extraction with Matrix Factorization and Universal Schemas [Riedel 13+]
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