Foundations of Statistical NLP Chapter 9. Markov Models 한 기 덕한 기 덕

Foundations of Statistical NLP

Chapter 9. Markov Models

한 기 덕

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Contents

Introduction Markov Models Hidden Markov Models

– Why use HMMs– General form of an HMM– The Three Fundamental Questions for HMMs

Fundamental Questions For HMMs Implementation, Properties, and Variants

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Introduction

Markov Model– Markov processes/chains/models were first developed by Andrei

A. Markov

– First use linguistic purpose : modeling the letter sequences in Russian literature(1913)

– Current use general statistical tool

VMM (Visible Markov Model)– Words in sentences is depend on their syntax.

HMM (Hidden Markov Model)– operate high level abstraction by postulating additional “hidden”

structures.

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Markov Models

Markov assumption– Future elements of the sequence independent of past ele

ments, given the present element.

Limited Horizon– Xt = sequence of random variables

– Sk = state space

Time invariant (stationary)

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Markov Models(Cont’)

Notation– stochastic transition

– probability of different initial state

Application : Linear sequence of events– modeling valid phone sequences in speech recognition

– sequences of speech acts in dialog systems

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Markov Chain

circle : state and state name arrows connecting states : possible transition arc label : probability of each transition

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Visible Markov Model

We know what states the machine is passing through.

mth order Markov model– n 3, n-gram violate Limited Horizen condition– reformulate any n-gram model as a visible Markov model by simpl

y encoding (n-1)-gram

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Hidden Markov Model

We don’t know the state sequence that the model passes through, only some probabilistic function of it

Example 1 : The crazy soft drink machine– two state : cola preferring(CP), iced tea preferring(IP)

– VMM : machine always put out a cola in CP

– HMM : emission probability

– Output probability given From state

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Crazy soft drink machine

Problem– What is the probability of seeing the output sequence {lem, ice-t} i

f the machine always start off in the cola preferring state?

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Crazy soft drink machine(Cont’)

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Why use HMMs?

underlying events probabilistically generate surface events– the words in a text parts of speech

Linear interpolation of n-gram

Hidden state– the choice of whether to use the unigram, bigram, or trigram proba

bilities.

Two Keys– This is conversion works by adding epsilon transitions.

– Separate parameters iab don’t adjust them separately.

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NotationA

B

AAA

BB

SSS

KKK

S

K

S

K

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General form of an HMM

Arc-emission HMM– the symbol emits at time t depends on both the state at time t and

at time(t+1). State-emission HMM : ex) crazy drink machine

– the symbol emits at time t depends just on the state at time t

Figure 9.4 A program for a Markov process.

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The Three Fundamental Questions for HMMS

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Finding the probability of an observation

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The forward procedure

Cheap algorithm required only 2N2T multiplication

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The backward procedure

The total probability of seeing the rest of the observation sequence.

Use of a combination of forward and backward probabilities is vital for solving the third problem of parameter reestimation.

Backward variables

Combining forward & backward

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Finding the best state sequence

State sequence that explains the observations is more than one way.

Find Xt that maximizes P(Xt|O, )

This may yield a quite unlikely state sequence.

Viterbi algorithm is more efficient.

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Viterbi algorithem

The most likely complete path

This is sufficient to maximize for a fixed O

Definition

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Variable calculations for O = (lem, ice_t, cola)

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Parameter estimation

Given a certain observation sequence Find the values of the model parameter

= (A, B, ) Using Maximum Likelihood Estimation

Locally maximize by an iterative hill-climbing algorithm usually effective for HMM

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Parameter estimation (Cont’)

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Parameter estimation (Cont’)

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Implementation, Properties, Variants

Implementation– Obvious issue : keeping on multiplying very small

numbers Use Log function

Variants– It is not impossible to estimate many number

parameter.

Multiple input observations Initialization of parameter values

– Try to approach near global maximum