Extracting Semantic Representations with Probabilistic Topic Models Mark SteyversUC Irvine Tom...

Extracting Semantic Representations

with Probabilistic Topic Models

Mark Steyvers UC Irvine

Tom Griffiths Padhraic Smyth

Dave Newman

Brown University UC IrvineUC Irvine

Extracting Statistical Regularities from Text

EMAIL

BOOKS/ JOURNALS

NEWSPAPERS

Computer Science/Statistics:Information retrieval

Text miningData mining

Psychology:Semantic cognitionEpisodic memoryPsycholinguistics

?

Overview

I Probabilistic Topic Models

II Computer Science ApplicationsAnalyzing Scientific Topics: PNASAnalyzing NSF and NIH fundingAnalyzing Enron Email

III Theory for semantic cognitionWord AssociationFree Recall

IV Conclusion

Probabilistic Topic Models

Originated in domain of statistics & machine learning (e.g., Hoffman, 2001; Blei, Ng, Jordan, 2003)

Extracts topics from large collections of text

No usage of dictionaries of thesauri

Topic extraction is unsupervised

DATACorpus of text

Topic Model

Find parameters that “reconstruct” data

Model is Generative

Probabilistic Topic Models

Each document is a probability distribution over topics

Each topic is a probability distribution over words

Document generation as a probabilistic process

TOPICS MIXTURETOPICS MIXTURE

TOPIC TOPIC TOPICTOPIC

WORDWORD WORDWORD

......

......

1. for each document, choosea mixture of topics

2. For every word slot, sample a topic [1..T] from the mixture

3. sample a word from the topic

loan

TOPIC 1

money

loan

bank

moneyb

an

k

river

TOPIC 2

river

river

stream

bank

bank

stream

bank

loan

DOCUMENT 2: river2 stream2 bank2 stream2 bank2 money1 loan1

river2 stream2 loan1 bank2 river2 bank2 bank1 stream2 river2 loan1

bank2 stream2 bank2 money1 loan1 river2 stream2 bank2 stream2 bank2 money1 river2 stream2 loan1 bank2 river2 bank2 money1 bank1 stream2 river2 bank2 stream2 bank2 money1

DOCUMENT 1: money1 bank1 bank1 loan1 river2 stream2 bank1

money1 river2 bank1 money1 bank1 loan1 money1 stream2 bank1

money1 bank1 bank1 loan1 river2 stream2 bank1 money1 river2 bank1

money1 bank1 loan1 bank1 money1 stream2 .3

.8

.2

Example

Mixture components

Mixture weights

Bayesian approach: use priors Mixture weights ~ Dirichlet( ) Mixture components ~ Dirichlet( )

.7

DOCUMENT 2: river? stream? bank? stream? bank? money? loan?

river? stream? loan? bank? river? bank? bank? stream? river? loan?

bank? stream? bank? money? loan? river? stream? bank? stream? bank? money? river? stream? loan? bank? river? bank? money? bank? stream? river? bank? stream? bank? money?

DOCUMENT 1: money? bank? bank? loan? river? stream? bank?

money? river? bank? money? bank? loan? money? stream? bank?

money? bank? bank? loan? river? stream? bank? money? river? bank?

money? bank? loan? bank? money? stream?

Inverting (“fitting”) the model

Mixture components

Mixture weights

TOPIC 1

TOPIC 2

?

?

?

Inverting the generative model

Inverting the model involves extracting topics and mixing proportions per document from corpus

Bayesian Inference techniques (MCMC with Gibbs sampling)

Example: topics from an educational corpus (TASA)

PRINTINGPAPERPRINT

PRINTEDTYPE

PROCESSINK

PRESSIMAGE

PRINTERPRINTS

PRINTERSCOPY

COPIESFORM

OFFSETGRAPHICSURFACE

PRODUCEDCHARACTERS

PLAYPLAYSSTAGE

AUDIENCETHEATERACTORSDRAMA

SHAKESPEAREACTOR

THEATREPLAYWRIGHT

PERFORMANCEDRAMATICCOSTUMES

COMEDYTRAGEDY

CHARACTERSSCENESOPERA

PERFORMED

TEAMGAME

BASKETBALLPLAYERSPLAYER

PLAYPLAYINGSOCCERPLAYED

BALLTEAMSBASKET

FOOTBALLSCORECOURTGAMES

TRYCOACH

GYMSHOT

JUDGETRIAL

COURTCASEJURY

ACCUSEDGUILTY

DEFENDANTJUSTICE

EVIDENCEWITNESSES

CRIMELAWYERWITNESS

ATTORNEYHEARING

INNOCENTDEFENSECHARGE

CRIMINAL

HYPOTHESISEXPERIMENTSCIENTIFIC

OBSERVATIONSSCIENTISTS

EXPERIMENTSSCIENTIST

EXPERIMENTALTEST

METHODHYPOTHESES

TESTEDEVIDENCE

BASEDOBSERVATION

SCIENCEFACTSDATA

RESULTSEXPLANATION

STUDYTEST

STUDYINGHOMEWORK

NEEDCLASSMATHTRY

TEACHERWRITEPLAN

ARITHMETICASSIGNMENT

PLACESTUDIED

CAREFULLYDECIDE

IMPORTANTNOTEBOOK

REVIEW

• 37K docs, 26K words• 1700 topics, e.g.:

Polysemy

PRINTINGPAPERPRINT

PRINTEDTYPE

PROCESSINK

PRESSIMAGE

PRINTERPRINTS

PRINTERSCOPY

COPIESFORM

OFFSETGRAPHICSURFACE

PRODUCEDCHARACTERS

PLAYPLAYSSTAGE

AUDIENCETHEATERACTORSDRAMA

SHAKESPEAREACTOR

THEATREPLAYWRIGHT

PERFORMANCEDRAMATICCOSTUMES

COMEDYTRAGEDY

CHARACTERSSCENESOPERA

PERFORMED

TEAMGAME

BASKETBALLPLAYERSPLAYERPLAY

PLAYINGSOCCERPLAYED

BALLTEAMSBASKET

FOOTBALLSCORECOURTGAMES

TRYCOACH

GYMSHOT

JUDGETRIAL

COURTCASEJURY

ACCUSEDGUILTY

DEFENDANTJUSTICE

EVIDENCEWITNESSES

CRIMELAWYERWITNESS

ATTORNEYHEARING

INNOCENTDEFENSECHARGE

CRIMINAL

HYPOTHESISEXPERIMENTSCIENTIFIC

OBSERVATIONSSCIENTISTS

EXPERIMENTSSCIENTIST

EXPERIMENTALTEST

METHODHYPOTHESES

TESTEDEVIDENCE

BASEDOBSERVATION

SCIENCEFACTSDATA

RESULTSEXPLANATION

STUDYTEST

STUDYINGHOMEWORK

NEEDCLASSMATHTRY

TEACHERWRITEPLAN

ARITHMETICASSIGNMENT

PLACESTUDIED

CAREFULLYDECIDE

IMPORTANTNOTEBOOK

REVIEW

Overview




IV Conclusion

37CDNA

AMINOSEQUENCE

ACIDPROTEINISOLATEDENCODING

CLONEDACIDS

IDENTITYCLONE

EXPRESSEDENCODES

RATHOMOLOGY

How do topics change over time?Analysis of dynamics:

perform linear trend analysis for each topic“hot topics” go up, “cold topics” go down

289KDA

PROTEINPURIFIED

MOLECULARMASS

CHROMATOGRA..POLYPEPTIDE

GELSDS

BANDAPPARENTLABELED

IDENTIFIEDFRACTIONDETECTED

75ANTIBODY

ANTIBODIESMONOCLONAL

ANTIGENIGGMAB

SPECIFICEPITOPEHUMANMABS

RECOGNIZEDSERA

EPITOPESDIRECTED

NEUTRALIZING

2SPECIESGLOBALCLIMATE

CO2WATER

ENVIRONMENTALYEARS

MARINECARBON

DIVERSITYOCEAN

EXTINCTIONTERRESTRIALCOMMUNITYABUNDANCE

134MICE

DEFICIENTNORMAL

GENENULL

MOUSETYPE

HOMOZYGOUSROLE

KNOCKOUTDEVELOPMENT

GENERATEDLACKINGANIMALSREDUCED

179APOPTOSIS

DEATHCELL

INDUCEDBCL

CELLSAPOPTOTICCASPASE

FASSURVIVAL

PROGRAMMEDMEDIATEDINDUCTIONCERAMIDE

EXPRESSION

37CDNA

AMINOSEQUENCE

ACIDPROTEINISOLATEDENCODING

CLONEDACIDS

IDENTITYCLONE

EXPRESSEDENCODES

RATHOMOLOGY

289KDA

PROTEINPURIFIED

MOLECULARMASS

CHROMATOGRA..POLYPEPTIDE

GELSDS

BANDAPPARENTLABELED

IDENTIFIEDFRACTIONDETECTED

75ANTIBODY

ANTIBODIESMONOCLONAL

ANTIGENIGGMAB

SPECIFICEPITOPEHUMANMABS

RECOGNIZEDSERA

EPITOPESDIRECTED

NEUTRALIZING

2SPECIESGLOBALCLIMATE

CO2WATER

ENVIRONMENTALYEARS

MARINECARBON

DIVERSITYOCEAN

EXTINCTIONTERRESTRIALCOMMUNITYABUNDANCE

134MICE

DEFICIENTNORMAL

GENENULL

MOUSETYPE

HOMOZYGOUSROLE

KNOCKOUTDEVELOPMENT

GENERATEDLACKINGANIMALSREDUCED

179APOPTOSIS

DEATHCELL

INDUCEDBCL

CELLSAPOPTOTICCASPASE

FASSURVIVAL

PROGRAMMEDMEDIATEDINDUCTIONCERAMIDE

EXPRESSION

1990 1992 1994 1996 1998 2000 20022

4

6

8

10

12

14x 10

-3

289

37

75P(t

opic

)

1990 1992 1994 1996 1998 2000 20020

0.002

0.004

0.006

0.008

0.01

179

2

134

year

P(t

opic

)

year year

Cold topics Hot topics

NOBEL 1987

NOBEL 2002

Overview




IV Conclusion

NSF & NIH grant abstracts

Analyze 22,000+ active grants during 2002 NIH – NIMH, NCI NSF – BIO, SBE

Visualize topic similarity between funding programs

What topics are funded?

Example topics

brain .101 children .153 hiv .121 schizophrenia .226fmri .054 child .089 intervention .064 patients .067

imaging .054 parent .038 risk .050 deficits .054functional .046 parents .032 sexual .043 schizophrenic .027

mri .033 family .032 prevention .037 psychosis .024subjects .033 families .022 aids .024 subjects .023

magnetic .031 early .020 interventions .018 psychotic .022resonance .029 problems .019 reduction .015 dysfunction .019

neuroimaging .028 mothers .017 behavior .015 abnormalities .017structural .018 risk .017 men .013 clinical .015

visual .075 memory .237 older .083 disease .102processing .048 working .049 adults .071 ad .074

sensory .035 memories .022 age .066 alzheimer .043spatial .034 tasks .022 elderly .041 diabetes .025

information .022 retrieval .021 geriatric .041 cardiovascular .016eye .020 encoding .020 life .039 insulin .015

stimuli .020 cognitive .019 aging .033 vascular .015object .019 processing .019 late .032 blood .013

objects .019 recognition .018 cognitive .028 clinical .012perception .018 performance .016 aged .022 individuals .012

VISUAL PROCESSING MEMORY AGING

ALZHEIMER DISEASE

BRAIN IMAGINGCHILD PARENT INTERACTION

HIV INTERVENTION SCHIZOPHRENIA

NCICancer biology,

detection and diagnosis

NCIAIDS Research

NCICancer

Research Centers

NCICancer

causationNCI

Cancer prevention and control

NCICancer

treatment

NCIResearch

manpower development

NIMHAIDS Research

NIMHExtramural research

NIMHIntramural research

BCSArchaeology,

archeometry, and ...

BCSBehavioral

and cognitive sciences - Other

BCSChild learning

and development

BCSCultural

anthropology

BCSEnvironmental social

and behavioral scienceBCSGeography

and regional science

BCSHuman cognition and perception

BCSInstrumentation

BCSLinguistics

BCSPhysical

anthropology

BCSSocial

psychology

INTAfrica, Near East, and South Asia

INTAmericas

INTCentral

and Eastern Europe

INTEast Asia

and Pacific

INTInternational

activities - Other

INTJapan

and Korea INTWestern Europe

SESDecision, risk,

and management science

SESMethodology, measures,

and statistics

SESEconomics

SESEthics

and values studies

SESInnovation

and organizational change

SESLaw

and social science

SESPolitical science

SESResearch on science

and technology

SESScience

and technology studies

SESSocial and economic

sciences - Other

SESSociologySES

Transformations to quality organizations

BIRBiological

infrastructure - Other

BIRHuman

resources

BIRInstrumentation

BIRResearch resources

DEBEcological

studies

DEBEnvironmental biology - Other

DEBSystematic

& population biology

IBNDevelopmental mechanisms

IBNIntegrative biology

and neuroscience - Other

IBNNeuroscience

IBNPhysiology

and ethology

MCBBiochemical

and biomolecular processes

MCBBiomolecular structure

& function

MCBCell biology

MCBGenetics

MCBMolecular and cellular biosciences - Other

PGRPlant genome research project

NIH

NSF – BIO

NSF – SBE

2D visualization of funding programs – nearby program support similar topics

Funding Amounts per Topic

We have $ funding per grant

We have distribution of topics for each grant

Solve for the $ amount per topic

What are expensive topics?

Funding % Interpretation3.47 research center2.87 cancer control2.26 mental health services2.01 clinical treatment1.87 cancer 1.73 gene sequencing1.61 risk factors1.56 children/parents1.51 tumors1.48 training program1.47 immunology1.43 disorders1.40 patient treatment

Funding % Interpretation0.60 conference/meetings0.56 theory0.55 public policy0.55 collaborative projects0.55 marine environment0.55 decision making0.55 ecological diversity0.53 sexual behavior0.52 markets0.51 science/technology0.49 computer systems0.45 language0.44 archaelogy

High $$$ topics Low $$$ topics

Overview




IV Conclusion

Enron email data 500,000 emails500,000 emails

5000 authors5000 authors

1999-20021999-2002

Enron topics

2000 2001 2002 2003

PERSON1

PERSON2

TEXANSWIN

FOOTBALLFANTASY

SPORTSLINEPLAYTEAMGAME

SPORTSGAMES

GODLIFEMAN

PEOPLECHRISTFAITHLORDJESUS

SPIRITUALVISIT

ENVIRONMENTALAIR

MTBEEMISSIONS

CLEANEPA

PENDINGSAFETYWATER

GASOLINE

FERCMARKET

ISOCOMMISSION

ORDERFILING

COMMENTSPRICE

CALIFORNIAFILED

POWERCALIFORNIAELECTRICITY

UTILITIESPRICESMARKET

PRICEUTILITY

CUSTOMERSELECTRIC

STATEPLAN

CALIFORNIADAVISRATE

BANKRUPTCYSOCALPOWERBONDSMOU

TIMELINEMay 22, 2000

Start of California

energy crisis

Overview




IV Conclusion

Semantic Memory

Semantic memory system might arise from the need to

1) predict what concepts are needed in what contexts

2) disambiguate uncertain information

Useful perspective for understanding various language and memory tasks

Word Association

CUE:

PLAY

RESPONSES:

FUN, BALL, GAME, WORK, GROUND, MATE, CHILD,

ENJOY, WIN, ACTOR

Modeling Word Association Word association modeled as prediction

Given that a single word is observed, what future other words might occur?

Under a single topic assumption:

z

nn zPzwPwP w||w| 11

Response Cue

Observed associates for the cue “play”

Word P( word ) Word P( word ) Word Cosine FUN .141 BALL .041 KICKBALL .558 GAME 42 BALL .134 GAME .039 VOLLEYBALL .519 BALL 33 GAME .074 CHILDREN .019 GAMES .492 CHILDREN 30 WORK .067 ROLE .014 COSTUMES .478 SCHOOL 27

GROUND .060 GAMES .014 DRAMA .469 ROLE 25 MATE .027 MUSIC .009 ROLE .465 WANT 24 CHILD .020 BASEBALL .009 PLAYWRIGHT .464 GAMES 23 ENJOY .020 HIT .008 FUN .454 MOTHER 23 WIN .020 FUN .008 ACTOR .448 THINGS 21

ACTOR .013 TEAM .008 REHEARSALS .445 MUSIC 21 FIGHT .013 IMPORTANT .006 GAME .445 HELP 20 HORSE .013 BAT .006 ACTORS .439 FUN 19

KID .013 RUN .006 CHECKERS .431 READ 18 MUSIC .013 STAGE .005 MOLIERE .429 DON 18

HUMANS

Model predictions from TASA corpus

Word P( word ) Word P( word ) Word Cosine FUN .141 BALL .041 KICKBALL .558 GAME 42 BALL .134 GAME .039 VOLLEYBALL .519 BALL 33 GAME .074 CHILDREN .019 GAMES .492 CHILDREN 30 WORK .067 ROLE .014 COSTUMES .478 SCHOOL 27

GROUND .060 GAMES .014 DRAMA .469 ROLE 25 MATE .027 MUSIC .009 ROLE .465 WANT 24 CHILD .020 BASEBALL .009 PLAYWRIGHT .464 GAMES 23 ENJOY .020 HIT .008 FUN .454 MOTHER 23 WIN .020 FUN .008 ACTOR .448 THINGS 21

ACTOR .013 TEAM .008 REHEARSALS .445 MUSIC 21 FIGHT .013 IMPORTANT .006 GAME .445 HELP 20 HORSE .013 BAT .006 ACTORS .439 FUN 19

KID .013 RUN .006 CHECKERS .431 READ 18 MUSIC .013 STAGE .005 MOLIERE .429 DON 18

HUMANS TOPICS (T=500)

RANK 9

Median rank of first associate

10

5

10

15

20

25

30

35

40Best LSA cosineBest LSA inner product1700 topics1500 topics1300 topics1100 topics900 topics700 topics500 topics300 topics

Med

ian

R

an

k

Latent Semantic Analysis(Landauer & Dumais, 1997)

word-document counts

high dimensional space

SVD

RIVERSTREAM

MONEY

BANK

Each word is a single point in semantic space Similarity measured by cosine of angle between

word vectors

Median rank of first associate

10

5

10

15

20

25

30

35

40Best LSA cosineBest LSA inner product1700 topics1500 topics1300 topics1100 topics900 topics700 topics500 topics300 topics

Med

ian

R

an

k

Triangle Inequality in Spatial Representations

w1

PLAY SOCCER

THEATER

Cosine similarity: cos(w1,w3) ≥ cos(w1,w2)cos(w2,w3) – sin(w1,w2)sin(w2,w3)

w2 w3

Testing violation of triangle inequality

Look for triplets of associates w1 w2 w3 such that

P( w2 | w1 ) >

P( w3 | w2 ) >

and measure P( w3 | w1 )

Vary threshold

Small-World Structure of Associations(Steyvers & Tenenbaum, 2005)

BASEBALL

BAT

BALL

GAME

PLAY

STAGE THEATER

Properties:1) Short path lengths2) Clustering3) Power law degree distributions

Small world graphs arise elsewhere: internet, social relations, biology

k = #incoming links

100 101 102 10310-5

10-4

10-3

10-2

10-1

100

P(

k )

#Incoming links has power law distribution

=-2.25

Power law degree distribution some words are very often used as an associate

BASEBALL

BAT

BALL

GAME

PLAY

STAGE THEATER

k = #incoming links100 101 102

10-4

10-3

10-2

10-1

100

P(

k )

d=50 d=200d=400

k = #incoming links

101 102

10-4

10-3

10-2

10-1

100

P(

k )

Creating Association Networks

TOPICS MODEL:

• Calculate the conditional probabilities of all word pairs i and j

• Connect i to j when P( w=j | w=i ) > threshold

LSA:

For each word, generate K associates by picking K nearest neighbors in semantic space

=-2.05

Paradigmatic/ Syntagmatic Associations

Associations in free recall

STUDY THESE WORDS: Bed, Rest, Awake, Tired, Dream, Wake, Snooze, Blanket, Doze, Slumber, Snore, Nap, Peace, Yawn, Drowsy

RECALL WORDS .....

FALSE RECALL: “Sleep” 61%

Recall as a reconstructive process

Reconstruct study list based on the stored “gist”

The gist can be represented by a distribution over topics

Under a single topic assumption:

znn zPzwPwP w||w| 11

Retrieved wordStudy list

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

BEDRESTTIRED

AWAKEWAKE

NAPDREAM

YAWNDROWSYBLANKETSNORE

SLUMBERPEACEDOZE

SLEEPNIGHT

ASLEEPMORNINGHOURS

SLEEPYEYESAWAKENED

Predictions for the “Sleep” list

STUDYLIST

EXTRALIST

(top 8)

w|1nwP

Psychology/Comp.Sci Connections

Research on human memory is useful for developing better text mining algorithms

Models for information retrieval might be helpful in understanding human memory

Integrating Topics and Syntax

Syntactic dependencies short range dependencies Semantic dependencies long-range

z z z z

w w w w

s s s s

Semantic state: generate words from topic model

Syntactic states: generate words from HMM

(Griffiths, Steyvers, Blei, & Tenenbaum, 2004)

...

INBY

WITHONAS

FROMTO

FOR

THEA

ANTHIS

THEIRITS

EACHONE

ISAREBE

HASHAVEWAS

WEREAS

BASEDPRESENTEDDISCUSSEDPROPOSEDDESCRIBED

SUCHUSED

DERIVED

THEORYMODEL

PROCESSESMODELSSYSTEM

PROCESSEFFECTS

INFORMATION

ATTENTIONSEARCHVISUAL

PROCESSINGTASK

PERFORMANCEINFORMATIONATTENTIONAL

MEMORYTERMLONG

SHORTRETRIEVALSTORAGE

MEMORIESAMNESIA

IQBEHAVIOR

EVOLUTIONARYENVIRONMENT

GENESHERITABILITY

GENETICSELECTION

DRUGAROUSALNEURALBRAIN

HABITUATIONBIOLOGICALTOLERANCEBEHAVIORAL

SOCIALSELF

ATTITUDEIMPLICIT

ATTITUDESPERSONALITY

JUDGMENTPERCEPTION

(S) THE SEARCH IN LONG TERM MEMORY ……

(S) A MODEL OF VISUAL ATTENTION ……

Random sentence generation

LANGUAGE:[S] RESEARCHERS GIVE THE SPEECH[S] THE SOUND FEEL NO LISTENERS[S] WHICH WAS TO BE MEANING[S] HER VOCABULARIES STOPPED WORDS[S] HE EXPRESSLY WANTED THAT BETTER VOWEL

Topic Hierarchies In regular topic model, no relations between

topics

Alternative: hierarchical topic organization topic 1

topic 2 topic 3

topic 4 topic 5 topic 6 topic 7

Nested Chinese Restaurant Process Blei, Griffiths, Jordan, Tenenbaum (2004) Learn hierarchical structure, as well as topics

within structure

Example: Psych Review Abstracts

RESPONSESTIMULUS

REINFORCEMENTRECOGNITION

STIMULIRECALLCHOICE

CONDITIONING

SPEECHREADINGWORDS

MOVEMENTMOTORVISUALWORD

SEMANTIC

ACTIONSOCIALSELF

EXPERIENCEEMOTION

GOALSEMOTIONALTHINKING

GROUPIQ

INTELLIGENCESOCIAL

RATIONALINDIVIDUAL

GROUPSMEMBERS

SEXEMOTIONS

GENDEREMOTIONSTRESSWOMENHEALTH

HANDEDNESS

REASONINGATTITUDE

CONSISTENCYSITUATIONALINFERENCEJUDGMENT

PROBABILITIESSTATISTICAL

IMAGECOLOR

MONOCULARLIGHTNESS

GIBSONSUBMOVEMENTORIENTATIONHOLOGRAPHIC

CONDITIONINSTRESS

EMOTIONALBEHAVIORAL

FEARSTIMULATIONTOLERANCERESPONSES

AMODEL

MEMORYFOR

MODELSTASK

INFORMATIONRESULTSACCOUNT

SELFSOCIAL

PSYCHOLOGYRESEARCH

RISKSTRATEGIES

INTERPERSONALPERSONALITY

SAMPLING

MOTIONVISUAL

SURFACEBINOCULAR

RIVALRYCONTOUR

DIRECTIONCONTOURSSURFACES

DRUGFOODBRAIN

AROUSALACTIVATIONAFFECTIVEHUNGER

EXTINCTIONPAIN

THEOF

ANDTOINAIS

Generative Process

RESPONSESTIMULUS

REINFORCEMENTRECOGNITION

STIMULIRECALLCHOICE

CONDITIONING

SPEECHREADINGWORDS

MOVEMENTMOTORVISUALWORD

SEMANTIC

ACTIONSOCIALSELF

EXPERIENCEEMOTION

GOALSEMOTIONALTHINKING

GROUPIQ

INTELLIGENCESOCIAL

RATIONALINDIVIDUAL

GROUPSMEMBERS

SEXEMOTIONS

GENDEREMOTIONSTRESSWOMENHEALTH

HANDEDNESS

REASONINGATTITUDE

CONSISTENCYSITUATIONALINFERENCEJUDGMENT

PROBABILITIESSTATISTICAL

IMAGECOLOR

MONOCULARLIGHTNESS

GIBSONSUBMOVEMENTORIENTATIONHOLOGRAPHIC

CONDITIONINSTRESS

EMOTIONALBEHAVIORAL

FEARSTIMULATIONTOLERANCERESPONSES

AMODEL

MEMORYFOR

MODELSTASK

INFORMATIONRESULTSACCOUNT

SELFSOCIAL

PSYCHOLOGYRESEARCH

RISKSTRATEGIES

INTERPERSONALPERSONALITY

SAMPLING

MOTIONVISUAL

SURFACEBINOCULAR

RIVALRYCONTOUR

DIRECTIONCONTOURSSURFACES

DRUGFOODBRAIN

AROUSALACTIVATIONAFFECTIVEHUNGER

EXTINCTIONPAIN

THEOF

ANDTOINAIS

Extracting Semantic Representations with Probabilistic Topic Models Mark SteyversUC Irvine Tom...

Documents

Transcript of Extracting Semantic Representations with Probabilistic Topic Models Mark SteyversUC Irvine Tom...