concepts & categorization. measurement of similarity geometric approach featural approach both...

Concepts & Categorization

Measurement of Similarity

• Geometric approach

• Featural approach

both are vector representations

Vector-representation for words

• Words represented as vectors of feature values• Similar words have similar vectors

98112…8129radio

12458…2462pet

22357…2361dog

12348…3461cat

98112…8129radio

12458…2462pet

22357…2361dog

12348…3461cat

How to get vector representations

• Multidimensional scaling on similarity ratings

• Tversky’s (1977) contrast model

• Latent Semantic Analysis(Landauer & Dumais, 1997)

• Topics Model(e.g., Griffiths & Steyvers, 2004)

Multidimensional Scaling (MDS) Approach

• Suppose we have N stimuli

• Measure the (dis)similarity between every pair of stimuli (N x (N-1) / 2 pairs).

• Represent each stimulus as a point in a multidimensional space.

• Similarity is measured by geometric distance, e.g., Minkowski distance metric:

rn

k

r

jkikij xxd1

1

Multidimensional Scaling

• Represent observed similarities by a multidimensional space – close neighbors should have high similarity

• Multidimensional Scaling: iterative procedure to place points in a (low) dimensional space to model observed similarities

Data: Matrix of (dis)similarity

MDS procedure: move points in space to best model observed similarity relations

Example: 2D solution for bold faces

2D solution for fruit words

Critical Assumptions of Geometric Approach

• Psychological distance should obey three axioms

– Minimality

– Symmetry

– Triangle inequality

0),(),(),( bbdaadbad

),(),( abdbad

),(),(),( cadcbdbad

For conceptual relations, violations of distance axioms often found

• Similarities can often be asymmetric

“North-Korea” is more similar to “China” than vice versa

“Pomegranate” is more similar to “Apple” than vice versa

• Violations of triangle inequality:

“Lemon”

“Orange” “Apricot”

Triangle Inequality and similarity constraints on words with multiple meanings

AB

BC

Euclidian distance: AC AB + BC

FIELD MAGNETIC

SOCCER

AC

Nearest neighbor problem (Tversky & Hutchinson (1986)

• In similarity data, “Fruit” is nearest neighbor in 18 out of 20 items

• In 2D solution, “Fruit” can be nearest neighbor of at most 5 items

• High-dimensional solutions might solve this but these are less appealing

Feature Contrast Model (Tversky, 1977)

• Represent stimuli with sets of discrete features

• Similarity is an – increasing function of common features– decreasing function of distinct features

)()()(),( IJcfJIbfJIafJISim Common features Features unique to I Features unique to J

a,b, and c are weighting parameters

Contrast model predicts asymmetries

Weighting parameter b > c

pomegranate is more similar to apple than vice versa becausepomegranate has fewer distinctive features

Contrast model predicts violations of triangle inequality

Weighting parameter a > b > c (common feature should be weighted more)

Additive Tree solution

Latent Semantic Analysis (LSA) Landauer & Dumais (1997)

Assumptions

1) words similar in meaning occur in similar verbal contexts(e.g., magazine articles, book chapters, newspaper articles)

2) we can count number of times words occur in documents and construct a large word x document matrix

3) this co-occurrence matrix contains a wealth of latent semantic information that can be extracted by statisticaltechniques

4) words can be represented as points in a multidimensionalspace

FIELD

GRASS

CORNBASEBALL

MAJOR FOOTBALL

Latent Semantic Analysis (Landauer & Dumais, ’97)

MEADOW

(high dimensional space)

1 2 … DFIELD 12 5 2

MEADOW 4BASEBALL 10

…MAJOR 5

DOCUMENTS

TE

RM

S

Information in matrix is compressed; relationships between words through other words are used.

Problem: LSA has to obey triangle inequality

AB

BC

Euclidian distance: AC AB + BC

FIELD MAGNETIC

SOCCER

AC

The Topics Model (Griffith & Steyvers, 2002 & 2003)

• A probabilistic version of LSA: no spatial constraints.

• Each document (i.e. context) is a mixture of topics. Each topic is a distribution over words

• Each word chosen from a single topic:

T

jiiii jzPjzwPwP

1

|

word probability in topic j

probability of topic jin document

P( w | z )HEART 0.3 LOVE 0.2SOUL 0.2TEARS 0.1MYSTERY 0.1JOY 0.1

P( z = 1 )

P( w | z )SCIENTIFIC 0.4 KNOWLEDGE 0.2WORK 0.1RESEARCH 0.1MATHEMATICS 0.1MYSTERY 0.1

P( z = 2 ) TOPIC MIXTURE

A toy example

MIXTURE COMPONENTS

wi

Words can occur in multiple topics

P( w | z )HEART 0.3 LOVE 0.2SOUL 0.2TEARS 0.1MYSTERY 0.1JOY 0.1

P( z = 1 ) = 1

P( w | z )SCIENTIFIC 0.4 KNOWLEDGE 0.2WORK 0.1RESEARCH 0.1MATHEMATICS 0.1MYSTERY 0.1

P( z = 2 ) = 0 TOPIC MIXTURE

All probability to topic 1…

MIXTURE COMPONENTS

wi

Document: HEART, LOVE, JOY, SOUL, HEART, ….

P( w | z )HEART 0.3 LOVE 0.2SOUL 0.2TEARS 0.1MYSTERY 0.1JOY 0.1

P( z = 1 ) = 0

P( w | z )SCIENTIFIC 0.4 KNOWLEDGE 0.2WORK 0.1RESEARCH 0.1MATHEMATICS 0.1MYSTERY 0.1

P( z = 2 ) = 1 TOPIC MIXTURE

All probability to topic 2 …

MIXTURE COMPONENTS

wi

Document: SCIENTIFIC, KNOWLEDGE, SCIENTIFIC, RESEARCH, ….

P( w | z )HEART 0.3 LOVE 0.2SOUL 0.2TEARS 0.1MYSTERY 0.1JOY 0.1

P( z = 1 ) = 0.5

P( w | z )SCIENTIFIC 0.4 KNOWLEDGE 0.2WORK 0.1RESEARCH 0.1MATHEMATICS 0.1MYSTERY 0.1

P( z = 2 ) = 0.5 TOPIC MIXTURE

Mixing topic 1 and 2

MIXTURE COMPONENTS

wi

Document: LOVE, SCIENTIFIC, HEART, SOUL, KNOWLEDGE, RESEARCH, ….

Application to corpus data

• TASA corpus: text from first grade to college– representative sample of text

• 26,000+ word types (stop words removed)

• 37,000+ documents

• 6,000,000+ word tokens

THEORYSCIENTISTS

EXPERIMENTOBSERVATIONS

SCIENTIFICEXPERIMENTSHYPOTHESIS

EXPLAINSCIENTISTOBSERVED

EXPLANATIONBASED

OBSERVATIONIDEA

EVIDENCETHEORIESBELIEVED

DISCOVEREDOBSERVE

FACTS

SPACEEARTHMOON

PLANETROCKET

MARSORBIT

ASTRONAUTSFIRST

SPACECRAFTJUPITER

SATELLITESATELLITES

ATMOSPHERESPACESHIPSURFACE

SCIENTISTSASTRONAUT

SATURNMILES

ARTPAINT

ARTISTPAINTINGPAINTEDARTISTSMUSEUM

WORKPAINTINGS

STYLEPICTURES

WORKSOWN

SCULPTUREPAINTER

ARTSBEAUTIFUL

DESIGNSPORTRAITPAINTERS

STUDENTSTEACHERSTUDENT

TEACHERSTEACHING

CLASSCLASSROOM

SCHOOLLEARNING

PUPILSCONTENT

INSTRUCTIONTAUGHTGROUPGRADE

SHOULDGRADESCLASSES

PUPILGIVEN

BRAINNERVESENSE

SENSESARE

NERVOUSNERVES

BODYSMELLTASTETOUCH

MESSAGESIMPULSES

CORDORGANSSPINALFIBERS

SENSORYPAIN

IS

CURRENTELECTRICITY

ELECTRICCIRCUIT

ISELECTRICAL

VOLTAGEFLOW

BATTERYWIRE

WIRESSWITCH

CONNECTEDELECTRONSRESISTANCE

POWERCONDUCTORS

CIRCUITSTUBE

NEGATIVE

A selection from 500 topics

FIELDMAGNETICMAGNET

WIRENEEDLE

CURRENTCOIL

POLESIRON

COMPASSLINESCORE

ELECTRICDIRECTION

FORCEMAGNETS

BEMAGNETISM

POLEINDUCED

SCIENCESTUDY

SCIENTISTSSCIENTIFIC

KNOWLEDGEWORK

RESEARCHCHEMISTRY

TECHNOLOGYMANY

MATHEMATICSBIOLOGY

FIELDPHYSICS

LABORATORYSTUDIESWORLD

SCIENTISTSTUDYINGSCIENCES

BALLGAMETEAM

FOOTBALLBASEBALLPLAYERS

PLAYFIELD

PLAYERBASKETBALL

COACHPLAYEDPLAYING

HITTENNISTEAMSGAMESSPORTS

BATTERRY

JOBWORKJOBS

CAREEREXPERIENCE

EMPLOYMENTOPPORTUNITIES

WORKINGTRAINING

SKILLSCAREERS

POSITIONSFIND

POSITIONFIELD

OCCUPATIONSREQUIRE

OPPORTUNITYEARNABLE

Polysemy: words with multiple meanings represented in different topics

No Problem of Triangle Inequality

SOCCER

MAGNETICFIELD

TOPIC 1 TOPIC 2

Topic structure easily explains violations of triangle inequality

How to get vector representations

• Multidimensional scaling on similarity ratings

• Tversky’s (1977) contrast model

• Latent Semantic Analysis(Landauer & Dumais, 1997)

• Topics Model(e.g., Griffiths & Steyvers, 2004)