David Graus - Entity Linking (at SEA), Search Engines Amsterdam, Fri June 27th
1. Entity Linking (at SEA)
David Graus, University of Amsterdam
Photo by TRPultz (Creative Commons Attribution 3.0 Unported License)
2. Entity Linking at SEA
Search Engines Amsterdam, 27 June ’14
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Today’s talk
Ò What?
Ò Why?
Ò How?
Ò Etc.
3. Entity Linking at SEA
Search Engines Amsterdam, 27 June ’14
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Entity Linking?
Ò Link mentions of entities (in text) to their
referent entities (in a KB)
Ò Example:
“During Tank Johnson’s tumultuous tenure with
the Bears, incidents with guns got him arrested,
jailed and suspended, and his close friend was
shot and killed in front of him after an altercation
at a Chicago bar.”
4. Entity Linking at SEA
Search Engines Amsterdam, 27 June ’14
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Entity Linking?
Ò Link mentions of entities (in text) to their
referent entities (in a KB)
Ò Example:
“During Tank Johnson’s tumultuous tenure with
the Bears, incidents with guns got him arrested,
jailed and suspended, and his close friend was
shot and killed in front of him after an altercation
at a Chicago bar.”
5. Entity Linking at SEA
Search Engines Amsterdam, 27 June ’14
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Entity Mention: Tank
TANK (VEHICLE)
Knowledge
Base (KB)
Document r
TANK
query q
?
?
TANK JOHNSON
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Search Engines Amsterdam, 27 June ’14
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Entity Search Outline
Ò What?
Ò Why?
Ò How?
Ò Etc.
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The Semanticizer
Ò Open source framework (https://github.com/semanticize/semanticizer/)
Ò Links to Wikipedia
Ò Entity = Wikipedia Page
Ò “Lexical matching” approach
Ò no NER, information extraction
http://semanticize.uva.nl/
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n-gram -> entity
Ò Kendrick Lamar
Ò K-Dot
Ò Kendrick
Ò K. Dot
Ò Kendrick Duckworth
Ò Kendrick Lamar'
Ò Kendrick Lamar's
Ò K Dot
Ò Kendrick Lama
Ò Kendrick Lamarr
Ò Kendrick Llama
Ò The Jig Is Up (Dump'n)
15. Entity Linking at SEA
Search Engines Amsterdam, 27 June ’14
Ò For an input sentence s;
!
!
!
!
Ò Retrieve all possible entity candidates
“Eminem Thinks Kendrick Lamar’s
good kid, m.A.A.d. city Was ‘Genius’”
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Start linking!
16. Entity Linking at SEA
Search Engines Amsterdam, 27 June ’14
Ò For an input sentence s;
!
!
!
!
Ò Retrieve) all possible entity candidates
“Eminem Thinks Kendrick Lamar’s
good kid, m.A.A.d. city Was ‘Genius’”
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Start linking!
http://en.wikipedia.org/wiki/Eminem
http://en.wikipedia.org/wiki/Good_(economics)
http://en.wikipedia.org/wiki/Lamar_County,_Alabama
http://en.wikipedia.org/wiki/Lamar_County,_Mississippi
http://en.wikipedia.org/wiki/Lamar_Advertising_Company
http://en.wikipedia.org/wiki/Kendrick,_Idaho
http://en.wikipedia.org/wiki/Good_Kid_Maad_City
http://en.wikipedia.org/wiki/Kendrick_Lamar
http://en.wikipedia.org/wiki/Kendrick_School
http://en.wikipedia.org/wiki/Lamar_Cardinals_basketball
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Search Engines Amsterdam, 27 June ’14
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Ranking entity candidates
Ò “Prior probabilities”
Ò link probability
Ò commonness
Ò sense probability
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Search Engines Amsterdam, 27 June ’14
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1. Link Probability
Ò “Kendrick Lamar” occurs 698x on Wikipedia
Ò as hyperlink: 501x
Ò no hyperlink: 197x
!
!
Ò “Kendrick” occurs 5.037x on Wikipedia
Ò as hyperlink: 24x
Ò no hyperlink: 5.014x
!
24
5.037
= 0,005
!
501
698
= 0,718
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3. Sense Probability
Ò no. of times n-gram links to entity
Ò over all occurrences of n-gram
!
2
5.037
= 0,0004Kendrick -> Kendrick_Lamar =
Kendrick Lamar -> Kendrick_Lamar =
!
500
698
= 0,716
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Ranking by prior probability
Works quite well for the bulk of times!
!
High accuracy reported on naive linking using only
“popularity ranking” [1]
!
!
[1] Heng Ji, Ralph Grishman, “Knowledge Base Population: Successful
Approaches and Challenges”, ACL 2011
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Beyond ranking: supervised linking
Ò Entity linking as binary classification
!
Ò Input:
Ò sentence s + set of target entities E
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Beyond ranking: supervised linking
“Eminem Thinks Kendrick Lamar’s
good kid, m.A.A.d. city Was ‘Genius’”
http://en.wikipedia.org/wiki/Eminem
http://en.wikipedia.org/wiki/Good_(economics)
http://en.wikipedia.org/wiki/Lamar_County,_Alabama
http://en.wikipedia.org/wiki/Lamar_County,_Mississippi
http://en.wikipedia.org/wiki/Lamar_Advertising_Company
http://en.wikipedia.org/wiki/Kendrick,_Idaho
http://en.wikipedia.org/wiki/Good_Kid_Maad_City
http://en.wikipedia.org/wiki/Kendrick_Lamar
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Beyond ranking: supervised linking
Ò Given a new sentence, for each candidate entity
e output probability of belonging to class:
Ò positive (= target), or
Ò negative (= no target)
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Features
Ò Local:
Ò link each entity mention separately
Ò Global:
Ò link all mentions in a document simultaneously,
to arrive at a coherent set of entities
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Global features
“Eminem Thinks Kendrick Lamar’s
good kid, m.A.A.d. city Was ‘Genius’”
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Global features
“[Eminem] Thinks [Kendrick Lamar]’s
[good kid, m.A.A.d. city] Was ‘Genius’”
http://en.wikipedia.org/wiki/Eminem
http://en.wikipedia.org/wiki/Good_(economics)
http://en.wikipedia.org/wiki/Lamar_County,_Alabama
http://en.wikipedia.org/wiki/Lamar_County,_Mississippi
http://en.wikipedia.org/wiki/Lamar_Advertising_Company
http://en.wikipedia.org/wiki/Kendrick,_Idaho
http://en.wikipedia.org/wiki/Good_Kid_Maad_City
http://en.wikipedia.org/wiki/Kendrick_Lamar
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“Relatedness”
Source:
Milne, D. and Witten, I.H. (2008) An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In WIKIAI'08.
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Local features: n-gram/KB
Ò n-gram features:
Ò link probability
Ò length of n-gram
Ò number of entity titles that contain n-gram
Ò entity features:
Ò entity’s number of inlinks
Ò entity’s number of outlinks
Ò number of redirect pages referring to entity
Ò n-gram+entity features:
Ò commonness
Ò sense probability
Ò edit distance between n-gram and entity title
Ò does n-gram contain entity title?
Ò does entity title contain n-gram?
Ò does title equal n-gram?
Ò TF of n-gram in entity document
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Local features: Text similarity
Ò Similarity between input sentence s
!
!
!
and entity candidate document (Wikipedia page)
!
Ò Kendrick_Lamar 0.4215
Ò Kendrick,_Idaho 0.1599
“Eminem Thinks Kendrick Lamar’s
good kid, m.A.A.d. city Was ‘Genius’”
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But
Ò Too slow in real life
Ò Solution:
set of linked entities (inlinks / outlinks) as “virtual
document”
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Related entity document
["Entertainment Weekly”, "Compton, California”, “California", “Rapping",
“songwriter", "Hip hop music”, "Top Dawg Entertainment”, "Aftermath
Entertainment”, "Interscope Records”, "Black Hippy”, "Dr. Dre”, "The Game
(rapper)”, "Jay Rock”, "J. Cole”, "Hip hop music”, "recording artist”, "Compton,
California”, "Carson, California","Top Dawg Entertainment","Aftermath
Entertainment","Interscope Records","West Coast hip hop","Supergroup
(music)","Black Hippy","rapper","Schoolboy Q","Jay Rock","Ab-Soul","Overly
Dedicated","independent album","Section.80","iTunes Store","Major record
label","Dr. Dre","Game (rapper)","Drake (entertainer)","Young Jeezy","Talib
Kweli","Busta Rhymes","E-40","Warren G”, …]
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Ò Similarity between sentence s and virtual
document as related entity approximation
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Supervised Linking
Ò Feature vector for each sentence-entity pair
Ò Train a Random Forest classifier
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Local vs. global
Ò Hybrid > Local | Global
Ò Local & Global > Hybrid
Ò Approaches are complementary
Ò Global preferred for highly ambiguous entity
mentions (i.e., short ones)
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Etc…
Ò Open challenges:
Ò out of KB entities
Ò Knowledge Base Creation
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Thanks!
!
!
!
!
!
!
David Graus
d.p.graus@uva.nl