directed graphical model memm as sequence classifier niket tandon tutor: martin theobald, date: 09...

DIRECTED GRAPHICAL MODEL MEMM AS SEQUENCE

CLASSIFIER

NIKET TANDON

Tutor: Martin Theobald , Date: 09 June, 2011

Why talk about them..

HMMs is a sequence classifier Hidden Markov models (HMMs) successfully applied to:

Part-of-speech tagging:

<PRP>He</PRP> <VB>attends</VB> <NNS>seminars</NNS> Named entity recognition:

<ORG>MPI</ORG> director <PRS>Gerhard Weikum</PRS>

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Agenda

HMM Recap MaxEnt Model HMM + MaxEnt ~= MEMM Training & Decoding Experimental results and discussion

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Agenda


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Markov chains and HMM

HMM and Markov chains extend FA States qi, Transitions b/w states Aij

Markov assumption:

P(arc leaving a node) sum to oneHMM

Markov Chain

Weighted FA

Finite Automation

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HMM (One additional layer of uncertainty. States are hidden!)

Think of causal factors in prob.model

States qi

Transitions between states Aij

Observation likelihood or emission probability B = bi(ot) observation ot being generated from state qi

Markov Assumption

Output independence assumption

Task: Given observation #ice creams, predice weather states

HMM characterized by…7

three fundamental problems: Computing likelihood (Given an HMM l = (A,B) and O, find P(O|l) Decoding Given HMM l = (A,B) and O, find best hidden seq Q Learning Given Q and O, learn HMM parameters A, B

Computing P(O|l) observation likelihood8

N hidden states , T observations N x N x .. N = NT sequences. Exponentially large! HHC, HCH, CHH.. Efficient algo is Forward Algorithm (uses a table for intermediate values)

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Forward Algorithm

Previous forward prob *

P(previous state to current state) *

P(observation ot given current state j)

at(j) is P( tth state is state j after seeing first t observations )

For many HMM application many aij are zero, thus reducing the space.

Finally, summing over the prob of every path

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Decoding: Viterbi Algorithm

Given HMM l = (A,B) and O, find most probable hidden seq Q

Choose the hidden state sequence with max observation likelihood on running fwd algo. Exponential!

An efficient alternative is: Viterbi

Previous viterbi path prob *

P(previous state to current state) *

P(observation ot given current state j)

Compare fwd algo (sum Vs max) Keep a pointer to best path that

brought us here (backtrace)

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Learning: Forward Backward Algorithm

Train transition prob A, emmission B Consider Markov chain, then B = 1.0 ( so compute A)

Max likelihood of aij = transitionsij / all transitions from i But, HMM has hidden states! Additional work , computed using EM style Forward Backward Algorithm.

sum over all successive values bt+1 weighted by aij and observation prob

Agenda


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Background - Maximum Entropy models

A second probabilistic machine learning framework

Maximum Entropy as a non-sequential, sequence classifier

Most common MaxEnt sequence classifier is Max Entropy Markov Model MEMM

First, we discuss non-seq MaxEnt

Max Entropy works by extracting set of features (on input), combining linearly, and then sum as exponent

You must know: linear regression,

This value is not bounded. So, need normalize it.

Instead of prob, compute odds (recall that if P(A) = 0.9, P(A’) = 0.1

then, odds of A = 0.9 /0.3 = 3

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Logistic Regression

Linear model to predict odds of y=true

Now lies between 0 and ∞ , but need lie between –∞ and +∞, so take log. Left func is called logit

Model of regression used to estimate not prob but logit of Prob is called logistic regresssion.

So, if linear func estimates logit: what is P(y=true)?

This function called logistic function, gives gives Logistic regression its name

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Learning Weights (w)

Unlike Linear Regression that minimizes squared loss on train set, Logistic Regression uses Conditional Max Likelihood estimation.

w that makes P(observed y values in training data) to be highest, given x.

For entire train set

Taking log

An unwieldy expression

Condensed form (why)and substitution

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How to solve the convex optim. Problem?

Several methods to solve convex optimization problem Later we explain an algorithm: Generalized Iterative Scaling Method called GIS

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MaxEnt

Until now two classes, when more, Logistic regression is called Multinomial Logistic Regression (or MaxEnt)

In MaxEnt Prob that y=c is:

Normalize to make prob

fi(c,x) means feature i of observation x for class c.

fi are not real valued but binary (more common in text processing)

Learning w is similar to logistic regression, with one change. MaxEnt learns very high weights, so

Let us see a classification problem..

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Niket/NNP is/BEZ expected/VBN to/TO talk/?? today/

f1 f2 f3 f4 f5 f6

VB(f) 0 1 0 1 1 0

VB(w)

.8 .01 .1

NN(f)

1 0 0 0 0 1

NN(w)

.8 -1.3

VB/NN?

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More complex features..

Word starting with capital letter (Day) is more likely to be NNP than a common noun (e.g. Independence Day)

But a capitalized word occurring at beginning is not more likely NNP (e.g. Day after day) But, MaxEnt would be by hand as below

Key to successful use of MaxEnt is design of appropriate features and feature combinations

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Why the name Maximum Entropy?

Suppose we tag a new word Prabhu with a model that makes fewest assumptions, imposing no constraints.

We get an equiprobable distribution

Suppose we had some training data from which we learnt set of possible tags for Prabhu are NN,JJ,NNS,VB

Since of the tags is correct so, P(NN)+ P(JJ)+ P(NNS)+ P(VB) = 1

NN JJ NNS

VB NNP

VBG IN CD

1/8 1/8 1/8 1/8 1/8 1/8 1/8 1/8

NN JJ NNS

VB NNP

VBG

IN CD

¼ ¼ ¼ ¼ 0 0 0 0

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Maximum Entropy

… of all possible distributions, the equiprobable distribution has the maximum entropy

p* = argmax H(p)

Solution to this is entropy of a MaxEnt model whose weights W maximize the likelihood of the training data!

Agenda


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HMM MEMM

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Why HMM is not sufficient

HMM based on probabilities P(tag|tag) and P(word|tag). For tagging unknown words, useful features include capitalization, the presence of

hyphens, word endings HMM is unable to use information from later words to inform its decision early on. MEMM (Maximum Entropy Markov Model) mates Viterbi algorithm with

MaxEnt to overcome this problem!

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HMM (Generative) Vs MEMM (Discriminative)

HMM: two probabilities for the observation likelihood and prior.

MEMM: single probability, conditioned on the previous state, observation.

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MEMM

MEMM can condition on many features of input (capitalization, morphology (ending in -s or -ed), as well as earlier words or tags).

HMM can’t as its likelihood based, and needs to compute likelihood of each feature.

HMM

MEMM

Agenda


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Decoding(inference) and Learning in MEMM

Viterbi in HMM

Viterbi in MEMM

Learning in MEMM : train the weights so as maximize the log-likelihood of the training corpus. GIS!

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Learning: GIS basics

estimate a probability distribution p(a,b) where a defines classes (y1,y2) and b is feature indicator (0,1)

p(y1,0)+p(y2 ,0) = 0.6

Constraint on model’s expectation

Ep f = 0.6 (Expectation of feature)

p(y1,0)+p(y1 ,1)+ p(y2,0)+p(y2 ,1) = 1

Find prob distribution p with maxEnt

0 1

y1 ? ?

y2 ? ?

0 1

y1 .3 .2

y2 .3 .2

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GIS algorithm sketch

Finds weight parameter of the unique distribution p* belonging to P and Q

Each iteration of GIS requires model expectation and trainset expectation of features.

Train set expectation is a count of features. But, model expectation requires approximation.

Agenda


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Application: segmentation of FAQs

38 files belonging to 7 Usenet multi-part FAQs (set of files) Basic file structure:

header

text in Usenet header format

[preamble or table of content]

series of one of more question/answer pairs

tail

[copyright]

[acknowledgements]

[origin of document]

Formatting regularities: indentation, numbered questions, types of paragraph breaks Consistent formatting within a single FAQ

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Train data

Lines in each file are hand-labeled into 4 categories: head, questions, answers, tail<head>Archive-name: acorn/faq/part2

<head>Frequency: monthly

<head>

<question> 2.6) What configuration of serial cable should I use

<answer>

<answer> Here follows a diagram of the necessary connections

<answer> programs to work properly. They are as far as I know t

<answer>

<answer> Pins 1, 4, and 8 must be connected together inside

<answer> is to avoid the well known serial port chip bugs. The

Prediction: Given a sequence of lines, a learner must return a sequence of labels.

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Boolean features of lines

The 24 line-based features used in the experiments are:

begins-with-number contains-question-mark

begins-with-ordinal contains-question-word

begins-with-punctuation ends-with-question-mark

begins-with-question-word first-alpha-is-capitalized

begins-with-subject indented

blank indented-1-to-4

contains-alphanum indented-5-to-10

contains-bracketed-number more-than-one-third-space

contains-http only-punctuation

contains-non-space prev-is-blank

contains-number prev-begins-with-ordinal

contains-pipe shorter-than-30

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Experiment setup

“Leave one out” testing: For each file in a group (FAQ), train a learner and test it on the remaining files in the group.

Scores are averaged over n(n-1) results.

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Evaluation metrics

Segment: consecutive lines belonging to the same category Co-occurrence agreement probability (COAP)

Empirical probability that the actual and the predicted segmentation agree on the placement of two lines according to some distance distribution D between lines.

Measures whether segment boundaries are properly aligned by the learner

Segmentation precision (SP):

Segmentation recall (SR):

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Comparison of learners

ME-Stateless: Maximum entropy classifier document is an unordered set of lines lines are classified in isolation using the binary features, not using label of previous line

TokenHMM: Fully connected HMM with hidden states for each of the four labels no binary features transitions between states only on line boundaries

FeatureHMM: same as TokenHMM lines are converted to sequences of features

MEMM

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Results

Learner COAP SegPrec SegRecall

ME-Stateless 0.52 0.038 0.362

TokenHMM 0.865 0.276 0.14

FeatureHMM 0.941 0.413 0.529

MEMM 0.965 0.867 0.681

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Problems with MEMM

Label bias problem Lead to CRF and further models (more on this in a while)

Thank you!

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