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Artificial Intelligence - Machine Learning in Natural Language Processing

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ML in NLP June 02

NASSLI 1

Machine Learning in Natural

Language Processing

Fernando Pereira

University of Pennsylvania

NASSLLI, June 2002

Thanks to

: William Bialek, John Lafferty, Andrew McCallum, Lillian Lee,

Lawrence Saul, Yves Schabes, Stuart Shieber, Naftali Tishby

ML in NLP

Introduction

ML in NLP

Why ML in NLP

n Examples are easier to create than rules

n Rule writers miss low frequency cases

n Many factors involved in language interpretation

n People do it

n AI

n Cognitive science

n Let the computer do it

n Moore’s law

n storage

n lots of data

ML in NLP

Classification

n Document topic

n Word sense

treasury

bonds ´

chemical

bonds

politics, business national, environment

ML in NLP June 02

NASSLI 2

ML in NLP

N(bin)

S(dumped)

NP-C(workers)

N(workers)

workers

VP(dumped)

V(dumped)

dumped

NP-C(sacks)

N(sacks)

sacks

PP(into)

P(into) NP-C(bin)

D(a)

a bin

into

Analysis

n Tagging

n Parsing

laterdecadesupshowthatsymptomscausing

JJNNSRPVBPWDTNNSVBG

ML in NLP

Language Modeling

n Is this a likely English sentence?

n Disambiguate noisy transcription

It’s easy to wreck a nice beach

It’s easy to recognize speech

P(colorless green ideas sleep furiously)

P(furiously sleep ideas green colorless)

ª 2 ¥ 10

ML in NLP

Inference

n Translation

n Information extraction

ligações

covalentes

ﬁ

covalent

bonds

obrigações

do tesouro

ﬁ

treasury

bonds

Sara Lee to Buy 30% of DIM

Chicago, March 3 - Sara Lee Corp said it agreed to buy a 30 percent interest in

Paris-based DIM S.A., a subsidiary of BIC S.A., at cost of about 20 million

dollars. DIM S.A., a hosiery manufacturer, had sales of about 2 million dollars.

The investment includes the purchase of 5 million newly issued DIM shares

valued at about 5 million dollars, and a loan of about 15 million dollars, it said.

The loan is convertible into an additional 16 million DIM shares, it noted.

The proposed agreement is subject to approval by the French government, it

said.

acquirer

acquired

ML in NLP

Machine Learning Approach

n Algorithms that

write programs

n Specify

• Form of output programs

• Accuracy criterion

Input

: set of training examples

Output

: program that performs as accurately

as possible on the training examples

But will it work on new examples?

ML in NLP June 02

NASSLI 3

ML in NLP

Fundamental Questions

Generalization

: is the learned program useful on new

examples?

Statistical learning theory

: quantifiable tradeoffs between

number of examples

complexity

of program class, and

generalization error

Computational tractability

: can we find a good program

quickly?

n If not, can we find a good approximation?

Adaptation

: can the program learn quickly from new

evidence?

n Information-theoretic analysis: relationship between adaptation

and compression

ML in NLP

Learning Tradeoffs

Program class complexity

Error of best program

training

testing on new examples

Overfitting

Rote learning

ML in NLP

Machine Learning Methods

n Classifiers

n Document classification

n Disambiguation disambiguation

n Structured models

n Tagging

n Parsing

n Extraction

n Unsupervised learning

n Generalization

n Structure induction

ML in NLP

Jargon

Instance

: event type of interest

n Document and its class

n Sentence and its analysis

n …

Supervised learning

: learn

classification

function

from hand-labeled instances

Unsupervised learning

: exploit correlations to

organize training instances

Generalization

: how well does it work on

unseen data

Features

: map instance to set of elementary

events

ML in NLP June 02

NASSLI 4

ML in NLP

Classification Ideas

n Represent instances by

feature

vectors

n Content

n Context

n Learn function from feature vectors

n Class

n Class-probability distribution

n Redundancy is our friend:

many weak

clues

ML in NLP

Structured Model Ideas

Interdependent

decisions

n Successive parts-of-speech

n Parsing/generation steps

n Lexical choice

• Parsing

• Translation

n Combining decisions

n Sequential decisions

n Generative models

n Constraint satisfaction

ML in NLP

Unsupervised Learning Ideas

Clustering

: class induction

Latent

variables

I’m thinking of sports ﬁ more sporty words

Distributional

regularities

• Know words by the company they keep

n Data compression

n Infer

dependencies among variables

structure learning

ML in NLP

Methodological Detour

n Empiricist/information-theoretic view:

words combine following their associations

in previous material

n Rationalist/generative view:

words combine according to a formal

grammar in the class of possible natural-

language grammars

ML in NLP June 02

NASSLI 5

ML in NLP

Chomsky’s Challenge to Empiricism

(1) Colorless green ideas sleep furiously.

(2) Furiously sleep ideas green colorless.

… It is fair to assume that neither sentence (1) nor

(2) (nor indeed any part of these sentences) has

ever occurred in an English discourse. Hence, in

any statistical model for grammaticalness, these

sentences will be ruled out on identical grounds as

equally ‘remote’ from English. Yet (1), though

nonsensical, is grammatical, while (2) is not.

Chomsky 57

ML in NLP

The Return of Empiricism

n Empiricist methods

work

n Markov models can capture a surprising fraction of

the unpredictability in language

n Statistical information retrieval methods beat

alternatives

n Statistical parsers are more accurate than

competitors based on rationalist methods

n Machine-learning, statistical techniques close to

human performance in part-of-speech tagging, sense

disambiguation

Just engineering tricks?

ML in NLP

Unseen Events

n Chomsky’s implicit assumption:

any model must

assign zero probability to unseen events

n naïve estimation of Markov model probabilities from

frequencies

n no

latent (hidden)

events

n Any such model

overfits

data: many events are

likely to be missing in any finite sample

\ The learned model

cannot generalize

to unseen

data

\ Support for

poverty of the stimulus

arguments

ML in NLP

The Science of Modeling

n Probability estimates can be

smoothed

accommodate unseen events

Redundancy

in language supports

effective statistical inference procedures

the stimulus is richer than it might seem

Statistical learning theory

generalization

ability

of a model class can be measured

independently of

model representation

Beyond Markov models:

effects of

latent

conditioning variables

can be estimated

from data

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mzg12345678

2017-11-24

难得的好书
guoxze

2014-03-19

介绍自然语言处理与机器学习的非常好的书，值得一看

gongyg1

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