david graus - entity linking (at sea), search engines amsterdam, fri june 27th
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TRANSCRIPT
Entity Linking (at SEA)David Graus, University of Amsterdam
Photo by TRPultz (Creative Commons Attribution 3.0 Unported License)
Entity Linking at SEA Search Engines Amsterdam, 27 June ’14 2
Today’s talk
Ò What? Ò Why? Ò How? Ò Etc.
Entity Linking at SEA Search Engines Amsterdam, 27 June ’14 3
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.”
Entity Linking at SEA Search Engines Amsterdam, 27 June ’14 4
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.”
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Entity Mention: Tank
TANK (VEHICLE)
KnowledgeBase (KB)
Document r
TANKquery q
?
?
TANK JOHNSON
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Entity Search Outline
Ò What? Ò Why? Ò How? Ò Etc.
Entity Linking at SEA Search Engines Amsterdam, 27 June ’14 7
Entity Linking at SEA Search Engines Amsterdam, 27 June ’14 8
Social Media Monitoring
Entity Linking at SEA Search Engines Amsterdam, 27 June ’14 9
Entity Linking at SEA Search Engines Amsterdam, 27 June ’14 10
Entity Search Outline
Ò What? Ò Why? Ò How? Ò Etc.
Entity Linking at SEA Search Engines Amsterdam, 27 June ’14 11
Entity Linking at SEA Search Engines Amsterdam, 27 June ’14 12
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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Lexical matching
Ò Construct “entity dictionaries” Ò By taking entity Titles
!
!Ò Anchors
!
!Ò Redirect pages
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n-gram -> entityÒ 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)
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!
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,_Alabamahttp://en.wikipedia.org/wiki/Lamar_County,_Mississippi
http://en.wikipedia.org/wiki/Lamar_Advertising_Company
http://en.wikipedia.org/wiki/Kendrick,_Idahohttp://en.wikipedia.org/wiki/Good_Kid_Maad_City
http://en.wikipedia.org/wiki/Kendrick_Lamarhttp://en.wikipedia.org/wiki/Kendrick_School
http://en.wikipedia.org/wiki/Lamar_Cardinals_basketball
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Ranking entity candidates
Ò “Prior probabilities” Ò link probability Ò commonness Ò sense probability
Entity Linking at SEA 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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2. “Commonness”
Ò “Kendrick” is used to refer to: Ò Kendrick,_Idaho Ò Kendrick,_Oklahoma Ò T._D._Kendrick Ò Kendrick_School Ò John_Kendrick_(American_sea_captain) Ò Kendrick_Lamar Ò Francis_Kenrick Ò Kendrick Ò Howie Kendrick
!8 3 3 2 2 2 2 1 1
!/ 24 / 24 / 24 / 24 / 24 / 24 / 24 / 24 / 24
!= 0,333 = 0,125 = 0,125 = 0.083 = 0.083 = 0.083 = 0.083 = 0.042 = 0.042
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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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Features: Semanticizer
Ò Local: Ò n-gram Ò KB Ò n-gram+KB Ò Text similarity
Ò Global: Ò Finding “related entities”
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Local features: n-gram/KBÒ 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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Global features
query q
Document r
query q
Document rCand.
e1
Cand.
e2
query q
Document rCand.e1
Cand. e2
Outlinke3
Inlinke4
Inlinke5
Outlinke6
Inlinke7
query q
Document rCand.e1
Cand. e2
Outlinke3
Inlinke4
Inlinke5
Outlinke6
Inlinke7
Anchor 3 Anchor 4
Anchor 3A
Anchor 3B
Anchor 5A
Anchor 5B
Anchor 5C
Anchor 4A
Anchor 4B
Anchor 2A
Anchor 2B
Anchor 2C
Anchor 1B
Anchor 1 Anchor 2
Anchor 1A
query q
Document rCand.e1
Cand. e2
Outlinke3
Inlinke4
Inlinke5
Outlinke6
Inlinke7
Anchor 3 Anchor 4
Anchor 3A
Anchor 3B
Anchor 5A
Anchor 5B
Anchor 5C
Anchor 4A
Anchor 4B
Anchor 2A
Anchor 2B
Anchor 2C
Anchor 1B
Anchor 1 Anchor 2
Anchor 1A
SupportAnchor 1A
SupportAnchor 5C
SupportAnchor 4B
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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!
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David Graus [email protected]