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transcript
Connections between the Lines:
Augmenting Social Networks with Text
Jonathan Chang, Jordan Boyd-Graber, David M. Blei
KDD 09
June 29th, 2009
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Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Motivation
Data that express relationships between ensembles of entities
(people, places, companies, genes, etc.) is pervasive.
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Motivation
Annotating the edges of these networks with rich information is
often a desideratum.
Jonathan Chang
Jordan Boyd-GraberYou and Jordan both went to Princeton.
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Motivation
Unfortunately, much of the rich information about links is encoded
implicitly as free text.
Example
In 1938, Wyman co-starred with Ronald Reagan. Reagan and actressJane Wyman were engaged at the Chicago Theater and married inGlendale, California. Following arguments about Reagan's politicalambitions, Wyman �led for divorce in 1948. Since Reagan is the onlyU.S. president to have been divorced, Wyman is the only ex-wife of anAmerican President.
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Motivation
Using the text as input, our goal is to annotate a network with this
information.
Example
Ronald Reagan
Jane WymanYou and Jane used to be married.
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Main idea
We de�ne a probabilistic model Networks Uncovered By
Bayesian Inference (Nubbi).
Using Nubbi, we can create a social network by discovering
relations over the ensemble of people.
Because Nubbi is a fully Bayesian approach, we can
incorporate information from other data-mining approaches as
priors (Agichtein and Gravano 2003; Diehl et al. 2007; Mei
et al. 2007; Sahay et al. 2008; Banko et al. 2007; Katrenko
and Adriaans 2007; Davidov et al. 2007).
Wikipedia
Joseph Stalin
Winston Churchill
Lyndon B. Johnson
Mao Zedong
Jimmy Carter
Margaret Thatcher
Ronald Reagan
Richard Nixon
Nikita KhrushchevJohn F. KennedyHubert
Humphrey
George H. W. Bush
Ross Perot
Leon Trotsky
Lev Kamenev
Zhou Enlai
Mikhail Gorbachev
labourgovernleaderbritishworld
sovietcommunist
centralunion
full
sovietrussiangovernunion
nuclear
republicanstate
federalistvotevice
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
A larger example...
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ylaroosevelt, franklin delanow
eill, sanford ikerkorian, kirkm
uhamm
ad, john allennajibullahw
illiams, jayson
dodd, christopher jtaylor, charlescone, davidpol poto'connor, sandra daygoldm
an, ronald lyleerlanger, stevenking, stephenonassis, jacqueline kennedygoodnough, abbyharris, ericsolom
on, deborahsandom
ir, richardkaye, judith slay, kenneth lrandolph, w
illiecarter, vincestrauss, richardcham
orro, violeta barrios decorzine, jon sholbrooke, richard csinatra, frankbuchanan, patrick jgrasso, richard aahm
adinejad, mahm
oudw
ashington, georgebush, georgefox quesada, vicentegram
m, phil
spitzer, eliotfitzw
ater, marlin
sorenstam, annika
edwards, herm
anjackson, m
ichaelvan natta, don jrtrum
an, harry sabbas, m
ahmoud
cooper, michael
diller, barryboss, kennethseles, m
onicam
andela, nelson r
rumsfeld, donald h
weinstein, jack b
gingrich, newt
aspin, lesm
enem, carlos saul
kissinger, henry a
cage, johnbabbitt, bruce
schumer, charles e
assad, bashar al−
boutros−ghali, boutros
simm
s, phillipton, eric
newm
an, paul
mcgw
ire, mark
lee, wen ho
leahy, patrick j
grassley, charles e
libeskind, daniel
gephardt, richard a
brown, lee p
vacco, dennis c
rell, m jodi
tyson, mike
davis, grayo'donnell, rosie
roberts, john g jr
clinton, hillary rodham
goetz, bernhard hugo
stephanopoulos, george
aziz, tariqlew
insky, monica
iacocca, lee a
feiner, paul
smith, w
illiam kennedy
cunningham, m
erce
khomeini, ruhollah
skilling, jeffrey k
showalter, buck
samaranch, juan antonio
abraham, spencer
springsteen, bruce
davenport, lindsay
monroe, m
arilyn
rohde, david
gonzales, alberto r
romney, m
itt
lemieux, m
ario
trump, donald j
pearl, daniel
jackson, bo
kerik, bernard b
mason, c vernon
lewis, carl
gulotta, thomas s
maazel, lorin
lugar, richard g
lincoln, abraham
de la hoya, oscar
lewis, michael
jackson, jesse l
berlusconi, silvio
suhartowachtler, sol
aoun, michel
lendl, ivan
rushdie, salman
mandela, nelson
holden, stephen
chen shui−bian
albright, madeleine k
gotbaum, betsy
hanover, donna
tenet, george j
cardoso, fernando henrique
piniella, lou
sampras, pete
kimmelman, michael
walesa, lech
mozart, wolfgang amadeus
barkley, charles
levy, harold o
byrd, robert c
jones, roy jr
shays, christopher
cohen, william s
mueller, robert s iii
mitterrand, francois
ginsburg, ruth bader
elizabeth ii, queen of great britain
williams, venus
brodeur, martin
el−hage, wadih
aristide, jean−bertrand
feingold, russell d
hawkins, yusuf k
beethoven, ludwig van
carroll, sean
kerrey, bob
giuliani, rudolph w
cruise, tom
stewart, martha
john paul ii
berke, richard l
graf, steffi
mitchell, george j
chawla, kalpana
maxwell, robert
winfield, dave
o'rourke, andrew p
nelson, lemrick jr
rodgers, richard
kohl, helmut
koppel, ted
rubin, robert e
stern, howard
clinton, chelsea
wright, frank lloyd
d'amato, alfonse m
rich, frank
annan, kofi
jackson, phil
putin, vladimir v
irabu, hideki
weiner, anthony d
miller, gifford
sharpton, al
johnson, keyshawn
hubbell, webster l
egan, edward m
libby, i lewis jr
obama, barack
johnson, ben
roddick, andy
bin laden, osama
brozan, nadine
boesky, ivan f
connors, jimmy
schwarzenegger, arnold
white, mary jo
ryan, george
mattingly, don
weld, william f
sosa, sammy
silverstein, larry a
wells, david
mccool, william c
traub, james
bratton, william j
sweeney, john j
daschle, tom
milken, michael r
ryan, nolan
blumenthal, richard
moussaoui, zacarias
jordan, michael
vallone, peter f
dylan, bob
christopher, warren m
codey, richard j
pagones, steven a
dershowitz, alan m
hewitt, lleyton
selig, budassad, hafez al−
bach, johann sebastian
altman, lawrence k
gehry, frankzimmer, richard a
daley, richard m
broad, william jbrady, lois smithsimpson, nicole brown
zarqawi, abu musab al−
reeves, dantrump, donaldiverson, allenknoblauch, chuckking, martin luther jrkennedy, anthony mkasparov, garykoresh, davidbenedict xvimarcos, ferdinand eferrer, fernando
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Topic Models
Nubbi leverages the machinery of topic modeling (Blei et al.
2003; Hofmann 1999; Steyvers and Gri�ths 2007), Bayesian
mixture models of discrete data.
Topic models have emerged as powerful tools for unsupervised
analysis of large document collections.
The multinomial parameters of the mixture components are
known as \topics."
Example (NYTimes using LDA)
law art politics sports
lawyer music republican game
justice �lm democrat coach
judge artist senate player
investigate art campaign play
prosecutor ballet mayor match
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Model input
We take as input text whose references to entities have been
identi�ed, and convert this into a collection of documents.
1 When Jesus had spoken these words, he went forth with his disciples over the brook Cedron, where was a garden, into the which he entered, and his disciples.
2 And Judas also, which betrayed him, knew the place: for Jesus ofttimes resorted thither with his disciples.
3 Judas then, having received a band of men and officers from the chief priests and Pharisees, cometh thither with lanterns and torches and weapons.
4 Jesus therefore, knowing all things that should come upon him, went forth, and said unto them, Whom seek ye?
5 They answered him, Jesus of Nazareth. Jesus saith unto them, I am he. And Judas also, which betrayed him, stood with them.
6 As soon then as he had said unto them, I am he, they went backward, and fell to the ground.
7 Then asked he them again, Whom seek ye? And they said, Jesus of Nazareth.
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Model input
4 Jesus therefore, knowing all things that should come upon him, went forth, and said unto them, Whom seek ye?
5 They answered him, Jesus of Nazareth. Jesus saith unto them, I am he. And Judas also, which betrayed him, stood with them.
6 As soon then as he had said unto them, I am he, they went backward, and fell to the ground.
7 Then asked he them again, Whom seek ye? And they said, Jesus of Nazareth.
1 When Jesus had spoken these words, he went forth with his disciples over the brook Cedron, where was a garden, into the which he entered, and his disciples.
2 And Judas also, which betrayed him, knew the place: for Jesus ofttimes resorted thither with his disciples.
3 Judas then, having received a band of men and officers from the chief priests and Pharisees, cometh thither with lanterns and torches and weapons.
received band officers chief priests Pharisees lanterns
torches weapons
spoken words disciples brook Cedron garden enter disciples knowing things
seek asked seek NazarethJesus
Judas
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Model input
1 When Jesus had spoken these words, he went forth with his disciples over the brook Cedron, where was a garden, into the which he entered, and his disciples.
2 And Judas also, which betrayed him, knew the place: for Jesus ofttimes resorted thither with his disciples.
3 Judas then, having received a band of men and officers from the chief priests and Pharisees, cometh thither with lanterns and torches and weapons.
4 Jesus therefore, knowing all things that should come upon him, went forth, and said unto them, Whom seek ye?
5 They answered him, Jesus of Nazareth. Jesus saith unto them, I am he. And Judas also, which betrayed him, stood with them.
6 As soon then as he had said unto them, I am he, they went backward, and fell to the ground.
7 Then asked he them again, Whom seek ye? And they said, Jesus of Nazareth.
betrayed knew place disciples answered Nazareth saith betrayed
Jesusand
Judas
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Intuition
Both individuals and pairs of people can be described by
topics.
Nubbi hypothesizes that each word in a pair context describes
either one of the entities or their relationship.
In 1938, Wyman co-starred with Ronald Reagan. Reagan and
actress Jane Wyman were engaged at the Chicago Theater and
married in Glendale, California. Following arguments about
Reagan's political ambitions, Wyman �led for divorce in 1948.
Since Reagan is the only U.S. president to have been divorced,
Wyman is the only ex-wife of an American President.
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Intuition
Red words can be explained by a politics topic.
Blue words can be explained by an acting topic.
politics words can be attributed to Ronald Reagan.
acting words can be attributed to either
Ronald Reagan or Jane Wyman.
The remaining words characterize their relationship.
In 1938, Wyman co-starred with Ronald Reagan. Reagan and
actress Jane Wyman were engaged at the Chicago Theater and
married in Glendale, California. Following arguments about
Reagan's political ambitions, Wyman �led for divorce in 1948.
Since Reagan is the only U.S. president to have been divorced,
Wyman is the only ex-wife of an American President.
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Graphical Model
MNe Ne,e'Kθ
βθ
ψe,e'
αθ
w
z
θe
αψ
c
πe,e'
ηθz
w
απ
Kψ
βψ
ηψ
Ne'
z
w
θe'
...Ne''
z
w
θe''
N entity contexts M pair contexts Kψ relationship topicsKθ entity topics
These intuitions are encoded in this graphical model.
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Graphical Model
MNe Ne,e'Kθ
βθ
ψe,e'
αθ
w
z
θe
αψ
c
πe,e'
ηθz
w
απ
Kψ
βψ
ηψ
Ne'
z
w
θe'
...Ne''
z
w
θe''
N entity contexts M pair contexts Kψ relationship topicsKθ entity topics
We must then infer the posterior probability over the hidden
variables (e.g., the hidden topics and the assignments of
topics to people and relationships) given the data.
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Graphical Model
MNe Ne,e'Kθ
βθ
ψe,e'
αθ
w
z
θe
αψ
c
πe,e'
ηθz
w
απ
Kψ
βψ
ηψ
Ne'
z
w
θe'
...Ne''
z
w
θe''
N entity contexts M pair contexts Kψ relationship topicsKθ entity topics
The posterior lacks the structure for e�cient computation, so
exact posterior inference is intractable. We appeal instead to
mean-�eld variational inference (Jordan et al. 1999).
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Bible Topics
Relationships are described by relationship topics
(c.f. Bhattacharya et al. 2008; Newman et al. 2006; Rosen-Zvi
et al. 2004; Culotta et al. 2005; Rabbat et al. 2006).
Topics cluster words which are used to describe similar
relationships and pairs who participate in similar relationships.
People in the Bible
Topic 1 Topic 2
Jacob-Laban Adonizedek-Piram
Top pairs Miriam-Moses Abraham-Birsha
Ishmael-Sarah Birsha-Lot
wife king
abraham cave
Top words daughter lord
child smote
call great
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Describing relationships
Nubbi can predict words in each entity pair's text pro�le.
This describes the relationship between the entities.
●
●
●
●
6 8 10 12 14 16 18
−5.
9−
5.8
−5.
7−
5.6
−5.
5−
5.4
biological
Number of topics
Wor
d P
redi
ctio
n Lo
g Li
kelih
ood
●
●
●
●
6 8 10 12 14 16 18
−6.
5−
6.4
−6.
3−
6.2
−6.
1−
6.0
−5.
9
bible
Number of topics
●
●
●
●
6 8 10 12 14 16 18
−7.
5−
7.0
−6.
5
wikipedia
Number of topics
● Nubbi Author−Topic LDA Unigram Mutual
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Predicting entities
Nubbi can also predict which entities are best described by a text
pro�le.
●
●
●
●
6 8 10 12 14 16 18
−6.
0−
5.5
−5.
0−
4.5
−4.
0
biological
Number of topics
Ent
ity P
redi
ctio
n Lo
g Li
kelih
ood
●
●●
●
6 8 10 12 14 16 18
−6.
5−
6.0
−5.
5
bible
Number of topics
●
●
●●
6 8 10 12 14 16 18
−7.
0−
6.5
−6.
0−
5.5
−5.
0−
4.5
−4.
0
wikipedia
Number of topics
● Nubbi Author−Topic LDA Unigram Mutual
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
NY Times social network
lazio, rick arichter, mikewilder, l douglasforeman, georgemehta, zubinbhutto, benazirwright, jimolmert, ehudpinochet, augustohatch, orrin gberlin, irvingsouter, david htower, john ghusband, rick drisen, jamescharles, eleanorklebold, dylanrather, danmcmellon, edwardsaint laurent, yveshelms, jessespitzer, eliot lpanetta, leon ecolumbus, christopher
jesus christmcveigh, timothy jameso'neill, paulmartinez, pedrobarry, marion s jrels, erniechernomyrdin, viktor s
hynes, charles jewing, patrickmiers, harriet estein, andrew jaung san suu kyi, dawsilver, sheldonspecter, arlencoughlin, tomkennedy, john fitzgerald
rodman, dennischaney, donjames, caryn
lane, nathanhashimoto, ryutaro
lautenberg, frank r
giambi, jasonde klerk, f wmourning, alonzo
mapplethorpe, robert
glavine, tomwinfrey, oprahvan horn, keithjeter, derek
reagan, ronaldlimbaugh, rush
netanyahu, benjamin
law, bernard fsessions, william s
fujimori, alberto
ward, charliebarron, james
van gundy, jeff
musharraf, pervez
blair, jayson
valentine, bobby
rowland, john g
chass, murray
jacobs, marc
puccini, giacomo
gates, henry louis jr
staples, brent
baker, howard h jr
clark, wesley k
koch, edward i
canseco, jose
major, john
plame, valerie
o'neill, paul h
holyfield, evander
mills, ric
hard p
abdullahbush, la
ura
odeh, mohammed saddiq
kozlowski, l dennis
karadzic, radovan
salinas de gortari,
carlos
federer, roger
jospin, lio
nel
bennett, willia
m j
nader, ralph
jackson, m
ark
steinbrenner, g
eorge m 3d
steinberg, li
sa
velella
, guy j
moynihan, d
aniel patric
k
kahane, m
eir
khamenei, a
li
darman, ri
chard g
barber, tiki
friedman, th
omas l
bradley, bill
minaya, o
mar
goldin, harris
on j
hernandez,
orlando
brown, d
ave
reich, ro
bert b
smith
, willi
am k
scoppetta
, nich
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james
, sha
rpe
gore
, al
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niel a
rap
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s, ste
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cher
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aul d
clark
, laur
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, arth
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larry
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, jane
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, hed
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fightmatch
trial
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Examining predictions on solicited descriptions
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players
tennis
president
house
clinton
speaker
american
political
figures
case
accused
sexual
president
court
failed
supreme
reagan
time
countries
worldleaders
powers
worldleaders
person
president
general
attorney
president
stategeneral
attorney
washington
area
baseball
players
baseball
atlanta
president
republican
politicians
york
city
party
mayor
democratic
candidates
players
baseball
politicians
american
political
administration
figures
politicians
president
administration
defense
secretary
leaders
countries
republican
running
politicians
person
senators
president
state
leaders
palestinian
time
political
leaders
countries
court
candidates
supreme
americanpolitical
figures
party
democratic
electionpresidential
primary
president
ranpoliticians
leaders
countriesmiddle
eastern
politicians
yorkmayor
politicians
york
politicians
police
commissioner
jersey
baseball
owner
chief
staff
person
senators
part
east
process
middle
peacehusband
won
governor
ran
convicted
york
city
mayor
candidates
2001
state
politicians
president
state
secretary
york
city
politicians
executives disney
players
subject
iranpoliticians
president
vice
1988
candidates
president
political
south
africa
worldleaders
victim
leaders
countries
convicted
prime
israeli
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Examining predictions on solicited descriptions
1 2 3 4 5 6 7
12
34
56
7
Lower is betterLog Rank (Author−Topic Model)
Log
Ran
k (N
ubbi
)
baseball
africa
baseball
south
presidential
israeli
owner
chairman
terrorist
palestinianisraeli
ministerminister
olympic
palestinian
primeprimeprime
fed
israeliisraeli
democratic
peacepeace
trackiran
peaceparty
candidates
democraticiraq
primaryrussian
russia
candidates
presidentialpresidentialpresidential
stars
candidates
supreme
presidential
senatedemocratic
presidential
politicianspoliticians2001
case
running
administrationcourt
time
worldworld
presidentstatestate
governor
figure
nations
president
tennis
wife
opponents
disney
baseball
nations
york
brothers
president
brothers
married
tennistennis
wife
fatherfather
husband
wifewife
sex
husband
wifehusbandhusbandwifewife
sonson
sexual
husbandhusband
sex
husbandhusband
relationship
brother
husband
sexualsexual
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Conclusion
Networks of entities are a pervasive form of data.
There are deep meanings associated with the links in these
networks.
These meanings are often not encoded in the network itself,
but rather as free text elsewhere.
The Nubbi model allows us to take this text and infer
characterizations of the relationships between the ensemble of
entities.
This is an important step towards mining complete social
networks from free text.
Thanks!
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Wikipedia Topics
Nubbi learns topics associated with individual entities.
This clusters entities according to the words used to describe
them.
People in Wikipedia
Topic 1 Topic 2
Frederick Sanger Kate Winslet
Top Entities Svante Arrhenius Ringo Starr
William Ramsay Al Pacino
work �lm
universe music
Top Terms year album
develop award
society song
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Wikipedia Topics
Nubbi also learns topics associated with pairs of entities.
These topics cluster together pairs with similar relationships.
People in Wikipedia
Topic 1 Topic 2
Tyler-Roosevelt Perot-Bush
Top Pairs McKinley-Roosevelt J.Q. Adams-Monroe
Taft-Roosevelt J.Q. Adams-Clay
president republican
vice march
Top Terms roosevelt reagan
calvin vote
johnson state
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Graphical Model
MNe Ne,e'Kθ
βθ
ψe,e'
αθ
w
z
θe
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c
πe,e'
ηθz
w
απ
Kψ
βψ
ηψ
Ne'
z
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z
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N entity contexts M pair contexts Kψ relationship topicsKθ entity topics
1 For each entity e,1 Draw entity topic proportions �e � Dir(��);2 For each word associated with this entity's context,
1 Draw topic assignment ze;n � Mult(�e);2 Draw word we;n � Mult(��
ze;n).
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Graphical Model
MNe Ne,e'Kθ
βθ
ψe,e'
αθ
w
z
θe
αψ
c
πe,e'
ηθz
w
απ
Kψ
βψ
ηψ
Ne'
z
w
θe'
...Ne''
z
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N entity contexts M pair contexts Kψ relationship topicsKθ entity topics
1 For each pair of entities e; e 0,1 Draw relationship topic proportions e;e0 � Dir(� );2 Draw selector proportions �e;e0 � Dir(��);3 For each word associated with this entity pair's context,
1 Draw selector ce;e0;n � Mult(�e;e0);
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Graphical Model
MNe Ne,e'Kθ
βθ
ψe,e'
αθ
w
z
θe
αψ
c
πe,e'
ηθz
w
απ
Kψ
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Ne'
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N entity contexts M pair contexts Kψ relationship topicsKθ entity topics
1 If ce;e0;n = 1,
1 Draw topic assignment ze;e0;n � Mult(�e);2 Draw word we;e0;n � Mult(��
ze;e
0;n
):
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Graphical Model
MNe Ne,e'Kθ
βθ
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αθ
w
z
θe
αψ
c
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ηθz
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z
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N entity contexts M pair contexts Kψ relationship topicsKθ entity topics
1 If ce;e0;n = 2,
1 Draw topic assignment ze;e0;n � Mult(�e0);2 Draw word we;e0;n � Mult(��
ze;e
0;n
):
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
Graphical Model
MNe Ne,e'Kθ
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αθ
w
z
θe
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c
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ηψ
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w
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w
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N entity contexts M pair contexts Kψ relationship topicsKθ entity topics
1 If ce;e0;n = 3,
1 Draw topic assignment ze;e0;n � Mult( e;e0);2 Draw word we;e0;n � Mult(�
ze;e
0;n
):
Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines
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Jonathan Chang, Jordan Boyd-Graber, David M. Blei Connections between the Lines