Log-Linear Models in NLP Noah A. Smith Department of Computer Science / Center for Language and...

Log-Linear Models in NLP

Noah A. SmithDepartment of Computer Science /

Center for Language and Speech ProcessingJohns Hopkins [email protected]

Outline

Maximum Entropy principleLog-linear modelsConditional modeling for

classificationRatnaparkhi’s tagger Conditional random fieldsSmoothingFeature Selection

DataFor now, we’re

just talking about modeling data. No task.

How to assign

probability to each

shape type?

3.19

2.12

0 0

1.06

4.25

3.19

Maximum Likelihood

1.06

0 0

0 0

1.06

0 0

1.06

11 degrees of freedom (12 –

1).

How to smooth?

Fewer parameters?

Some other kinds of models

Color

Shape Size

Pr(Color, Shape, Size) = Pr(Color) • Pr(Shape | Color) • Pr(Size | Color, Shape)

0.5

0.5

0.125

0.375

0.500

0.125

0.375

0.500

large 0.000

small 1.000

large 0.333

small 0.667

large 0.250

small 0.750

large 1.000

small 0.000

large 0.000

small 1.000

large 0.000

small 1.000

11 degrees of freedom (1 + 4 +

6).

These two are the same!

These two are the same!


Color

Shape Size

Pr(Color, Shape, Size) = Pr(Color) • Pr(Shape) • Pr(Size | Color, Shape)

0.5

0.5

0.125

0.375

0.500


6).large 0.000

small 1.000

large 0.333

small 0.667

large 0.250

small 0.750

large 1.000

small 0.000

large 0.000

small 1.000

large 0.000

small 1.000


Color

Shape Size

Pr(Color, Shape, Size) = Pr(Size) • Pr(Shape | Size) • Pr(Color | Size)

large

0.667

0.333

small

0.462

0.538

large

0.333

0.333

0.333

small 0.077

0.385

0.538

large 0.375

small 0.625


4).

No zeroes here ...


Color

Shape Size

Pr(Color, Shape, Size) = Pr(Size) • Pr(Shape) • Pr(Color)

0.125

0.375

0.500

large 0.375

small 0.625


1).0.5

0.5

This is difficult.

Different factorizations affect:smoothing

# parameters (model size)model complexity

“interpretability”goodness of fit

...Usually, this

isn’t done empirically,

either!

Desiderata

•You decide which features to use.•Some intuitive criterion tells you how to use them in the model.•Empirical.

Maximum Entropy

“Make the model as uniform as possible ...

but I noticed a few things that I want to model ...

so pick a model that fits the data on those things.”

Occam’s Razor

One should not increase,

beyond what is necessary, the

number of entities

required to explain

anything.

Uniform model

small 0.083 0.083 0.083

small 0.083 0.083 0.083

large 0.083 0.083 0.083

large 0.083 0.083 0.083

Constraint: Pr(small) = 0.625

small 0.104 0.104 0.104

small 0.104 0.104 0.104

large 0.063 0.063 0.063

large 0.063 0.063 0.063

0.625

Pr( , small) = 0.048

small 0.024 0.144 0.144

small 0.024 0.144 0.144

large 0.063 0.063 0.063

large 0.063 0.063 0.063

0.625

0.048

Pr(large, ) = 0.125

small 0.024 0.144 0.144

small 0.024 0.144 0.144

large 0.063 0.063 0.063

large 0.063 0.063 0.063

0.625

?

0.048

Questions

Does a solution always exist?

Is there a way to express the

model succinctly?

Is there an efficient way to

solve this problem?

What to do if it

doesn’t?

Entropy

• A statistical measurement on a distribution.• Measured in bits. [0, log2||]• High entropy: close to uniform• Low entropy: close to deterministic• Concave in p.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1

The Max Ent Problem

0

0.5

1 0

0.5

1

0

0.5

1

1.5

p1p2

H

Max

The Max Ent Problem

objective function is H

probabilities sum to 1 ...

... and are nonnegative

expected feature value under the

model

expected feature value from the data

n constraints

picking a distribution

The Max Ent Problem

0

0.5

1 0

0.5

1

0

0.5

1

1.5

p1p2

H

About feature constraints

1 if x is small,

0 otherwise

1 if x is a small ,

0 otherwise

1 if x is large and

light,0 otherwise

Mathematical Magic

Max

constrained|| variables (p)concave in p

unconstrainedN variables (θ)concave in θ

What’s the catch?

The model takes on a specific, parameterized form.

It can be shown that any max-ent model must take this form.

Outline



Log-linear models

Log linear

Log-linear models

Unnormalized probability, or

weight

Partition function

One parameter (θi) for each feature.

Mathematical Magic

Max

constrained|| variables (p)concave in p

unconstrainedN variables (θ)concave in θ

Max ent problem

Log-linear ML problem

What does MLE mean?

Independence among examples

Arg max is the same in the log

domain

MLE: Then and Now

Directed models Log-linear models

Concave Concave

Constrained (simplex) Unconstrained

“Count and normalize”

(closed form solution)Iterative methods

Iterative Methods

• Generalized Iterative Scaling• Improved Iterative Scaling• Gradient Ascent• Newton/Quasi-Newton Methods

– Conjugate Gradient– Limited-Memory Variable Metric– ...

All of these methods are

correct and will converge to the right answer; it’s just a matter of

how fast.

Questions

Does a solution always exist?

Is there a way to express the

model succinctly?

Is there an efficient way to

solve this problem?

Yes, if the constraints come from the data.

Yes, many iterative methods.

Yes, a log-linear model.

Outline



Conditional Estimation

Classification Rule:

Training Objective:

examples labels

Maximum Likelihood

object

label

Conditional Likelihood

object

label

Remember:

log-linear models

conditional estimation

The Whole Picture

Directed models Log-linear models

MLE“Count &

Normalize”®

Unconstrained concave

optimization

CLEConstrained

concave optimization

Unconstrained concave

optimization

Log-linear models: MLE vs. CLE

Sum over all example types

all labels.

Sum over all labels.

Classification Rule

Pick the most probable label y:

We don’t need to compute the

partition function at test time!But it does need to

be computed during training.

Outline



Ratnaparkhi’s POS Tagger (1996)

• Probability model:

• Assume unseen words behave like rare words.– Rare words ≡ count < 5

• Training: GIS• Testing/Decoding: beam search

Features: common words

the

stories

about

well-heeled

communities

anddeveloper

s

DT

NNS IN JJ NNS CC NNS

about

IN

stories

IN

the

IN

well-heeled

IN

communities

INNNS INDT

NNS IN

Features: rare words

the

stories

about

well-heeled

communities

anddeveloper

s

DT

NNS IN JJ NNS CC NNS

about

JJ

stories

JJ

communities

JJ

and

JJIN JJNNS IN JJ

w...

JJ

we...

JJ

wel...

JJ

well...

JJ

...d

JJ

...ed

JJ

...led

JJ

...eled

JJ

...-...

JJ

The “Label Bias” Problem

born towealt

h

VBN TO NN

born torun

VBN TO VBborn to

wealth

VBN TO NN

born towealt

h

VBN TO NN

born towealt

h

VBN TO NN

born torun

VBN TO VB

born torun

VBN TO VB

born torun

VBN TO VB

born torun

VBN TO VB

born torun

VBN TO VB

born torun

VBN TO VB

(4)

(6)

Is this symptomati

c of log-linear

models?No!

Tagging Decisions

tag1tag1

tag2

tag2

tag2

tag3

tag3

tag3

tag3

tag3

tag3

tag3

At each decision point, the total weight is 1.

tagn

Choose the path with the greatest weight.

tag3

A

B

C

A

B

C

D

A

B

D

B

You must choose tag2 = B, even if B is a terrible tag

for word2.Pr(tag2 = B | anything at

all!) = 1

■

You never pay a penalty for it!

Tagging Decisions in an HMM

tag1tag1

tag2

tag2

tag2

tag3

tag3

tag3

tag3

tag3

tag3

tag3

At each decision point, the total weight can be 0.

tagn

Choose the path with the greatest weight.

tag3

A

B

C

A

B

C

D

A

B

D

B

You may choose to discontinue this path if B can’t tag word2. Or pay a

high cost.

■

Outline



Conditional Random Fields

• Lafferty, McCallum, and Pereira (2001)

• Whole sentence model with local features:

Simple CRFs as Graphs

PRP$ NN VBZ ADV

My cat begs silently

PRP$ NN VBZ ADV


Compare with an HMM:

Weights, added together

.

Log-probs, added

together.

What can CRFs do that HMMs can’t?

PRP$ NN VBZ ADV


ADV

...ly

VBZ

...s

An Algorithmic Connection

What is the partition function?

Total weight of all paths.

CRF weight training

• Maximize log-likelihood:

• Gradient:

Total weight of all paths.

Forward algorith

m.Expected feature values.

Forward-backwar

d algorith

m.

Forward, Backward, and Expectations

fk is the number

of firings; each

firing is at some position

Markovian property

forward weight backward weight

forward weight

Forward, Backward, and Expectations

forward weight backward weight

forward weight to final state = weight of all paths

Forward-Backward’s Clients

Training a CRF Baum-Welch

supervised(labeled data)

unsupervised

concave bumpy

converges to global max

converges to local max

max p(y | x)(conditional training)

max p(x) (y unknown)

A Glitch

• Suppose we notice that -ly words are always adverbs.

• Call this feature 7.

-ly words are all ADV; this is maximal.

The expectation can’t exceed the max (it can’t even reach it).The gradient will always be positive.

The Dark Side of Log-Linear Models

Outline



Regularization

• θs shouldn’t have huge magnitudes• Model must generalize to test data

• Example: quadratic penalty

Bayesian Regularization:Maximum A Posteriori

Estimation

Independent Gaussians Prior (Chen and Rosenfeld, 2000)

Independence

Gaussian

0-mean, identical variance.

Quadratic penalty!

Alternatives

• Different variances for different parameters• Laplacian prior (1-norm)

• Exponential prior (Goodman, 2004)

• Relax the constraints (Kazama & Tsujii, 2003)

All θk ≥ 0.

Not differentiable.

Effect of the penalty

unsmoothedGoodman's smoothing

θk

Kazama & Tsujii’s box constraints

The primal Max Ent problem:

Sparsity

• Fewer features → better generalization

• E.g., support vector machines

• Kazama & Tsujii’s prior, and Goodman’s, give sparsity.

Sparsity

-1 -0.5 0 0.5 1 1.5 2 2.5 3

Kazama & Tsujii's smoothingGoodman's smoothing

Gaussian smoothing

θk

penalty

Cusp; function is not differentiable

here.

Gradient is 0.

Outline



Feature Selection

• Sparsity from priors is one way to pick the features. (Maybe not a good way.)

• Della Pietra, Della Pietra, and Lafferty (1997) gave another way.

Back to the original example.

Nine features.

• f1 = 1 if , 0 otherwise








• f9 = 1 unless some other feature fires; θ9 << 0

What’s

wrong

here?

θi = log counti

The Della Pietras’ & Lafferty’s Algorithm

1. Start out with no features.2. Consider a set of candidates.

• Atomic features.• Current features conjoined with atomic features.

3. Pick the candidate g with the greatest gain:

4. Add g to the model.5. Retrain all parameters.6. Go to 2.

PRP$

Feature Induction: Example

PRP$ NN VBZ ADV


Atomic features:

NN VBZ ADV


Selected features:

Other candidates:

NN VBZ

PRP$ NNNN

cat

Outline



Conclusions

Probabilistic models:robustness

data-orientedmathematically understood

Hacks:explanatory power

exploit expert’s choice of features(can be) more data-oriented

Log-linear models

The math is beautiful and easy

to implement. You pick

the features; the rest is just math!

Thank you!

Log-Linear Models in NLP Noah A. Smith Department of Computer Science / Center for Language and...

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