Text-classification using Text-classification using Latent Dirichlet Latent Dirichlet

AllocationAllocation- intro graphical model- intro graphical model

Text-classification using Text-classification using Latent Dirichlet Latent Dirichlet

AllocationAllocation- intro graphical model- intro graphical model

Lei LiLei Lileili@csleili@cs

Outline• Introduction• Unigram model and mixture• Text classification using LDA • Experiments• Conclusion

Text ClassificationWhat class can you tell given a doc?

…………………… the New York

Stock Exchange……………………

America’s Nasdaq ………………………

Buy………………………

…………………… bank debtloan

interest billion

buy………………………

…………………… the New York

Stock Exchange……………………

America’s Nasdaq ………………………

Buy………………………

…………………… Iraq war

weapon armyAk-47bomb

………………………

finance

military

Why db guys care?• Could be adapted to model

discrete random variables– Disk failures– user access pattern– Social network, tags– blog

Document• “ bag of words”: no order on

words

• d=(w1, w2, … wN)

• wi one value in 1…V (1-of-V scheme)

• V: vocabulary size

Modeling Document• Unigram: simple multinomial dist• Mixture of unigram• LDA• Other: PLSA, bigram

Unigram Model for Classification

• Y is the class label,

• d={w1, w2, … wN}

• Use bayes rule: • How to model the

document given class• ~ Multinomial

distribution, estimated as word frequency

)0()0|()1()1|(

)()|()|(

YPYdPYPYdP

YPYdPdYP

N

ii YwPYdP

1

)|()|(

)|( YwP i

Y

wN

Unigram: exampleP(w|Y) bank debt interest war army weapon

finance 0.2 0.15 0.1 0.0001 0.0001 0.0001

military 0.0001 0.0001 0.0001 0.1 0.15 0.2

d = bank * 100, debt * 110, interest * 130, war * 1, army * 0, weapon * 0

P(finance|d)=?

P(military|d)=?

P(Y)

finance 0.6

military 0.4

Mixture of unigrams for classification

Y

wN

z

• For each class, assume k topics

• Each topic represents a multinomial distribution

• Under each topic, each word is multinomial

Unigram: example

d = bank * 100, debt * 110, interest * 130, war * 1, army * 0, weapon * 0

P(finance|d)=?

P(military|d)=?

P(Y)

finance 0.6

military 0.4

P(w|z,Y)

bank debt interest war army weapon

finance 0.01 0.15 0.1 0.0001 0.0001 0.0001

0.2 0.01 0.01 0.0001 0.0001 0.0001

military 0.0001 0.0001 0.0001 0.1 0.15 0.01

0.0001 0.0001 0.0001 0.01 0.01 0.2P(z|Y)

finance 0.3

0.7

military 0.5

0.5

Bayesian Network• Given a DAG• Nodes are random variables, or

parameters• Arrow are conditional probability

dependency• Given some prob on part nodes, there

are algorithm to infer values for other nodes

Latent Dirichlet Allocation

• Model a θ as a Dirichlet distribution, on α

• For n-th term wn:– Model n-th latent

variable zn as a multinomial distribution according to θ.

– Model wn as a multinomial distribution according to zn and β.

Variational inference for LDA

• Direct inference with LDA is HARD

• Approximation with variational distribution

• use factorized distribution on variational parameters γ and Φ to approximate posterior distribution of latent variables θand z.

Experiment• Data set: Reuters-21578, 8681 training

documents, 2966 test documents.• Classification task: “EARN” vs. “Non-

EARN” • For each document, learn LDA features

and classify with them (discriminative)

Result'bank' 'trade' 'shares' 'tonnes'

'banks' 'japan' 'company' 'mln'

'debt' 'japanese' 'stock' 'reuter'

'billion' 'states' 'dlrs' 'sugar'

'foreign' 'united' 'share' 'production'

'dlrs' 'officials' 'reuter' 'gold'

'government' 'reuter' 'offer' 'wheat'

'interest' 'told' 'common' 'nil'

'loans' 'government' 'pct' 'gulf'

most frequent words in each topic

Classification Accuracy

Comparison of Accuracy

Take Away Message• LDA with few topics and few training data

could produce relative better results• Bayesian network is useful to model multiple

random variable, nice algorithm for it, • Potential use of LDA:

– disk failure– database access pattern– user preference (collaborative filtering)– social network (tags)

Reference• Blei, D., Ng, A., Jordan, M.: Latent

Dirichlet allocation. Journal of machine Learning Research