Expectation Maximization (EM) are set of rules in statistics, which are an iterative approach for obtaining maximum a posteriori (MAP) or maximum likelihood approx. of considerations for statistical models, as this model are based on unnoticeable hidden variables. Copy the link given below and paste it in new browser window to get more information on Expectation Maximization:- http://www.transtutors.com/homework-help/statistics/expectation-maximization.aspx

1. 1
Expectation Maximization

2. 2
The General Concept
 Expectation step: to reconstruct hidden structure,
current parameters are used
 Maximization step: to re-estimate parameters,
that hidden structure is used
Guess of
unknown
parameters
(probabilities)
initial
guess
M step
Guess of unknown
hidden structure
(tags, parses,
weather)
E step

3. 3
Guess of
unknown
parameters
(probabilities)
M step
Observed structure
(words, ice cream)
In Case of Hidden Markov Models
Guess of unknown
hidden structure
(tags, parses, weather)
E step
initial
guess

4. 600.465 - Intro to NLP - J. Eisner 4
Guess of
unknown
parameters
(probabilities)
M step
Observed structure
(words, ice cream)
In Case of Hidden Markov Models
Guess of unknown
hidden structure
(tags, parses, weather)
E step
initial
guess

5. 600.465 - Intro to NLP - J. Eisner 5
Guess of
unknown
parameters
(probabilities)
M step
Observed structure
(words, ice cream)
Guess of unknown
hidden structure
(tags, parses, weather)
E step
initial
guess
In Case of Hidden Markov Models

6. 6
In Case of Grammar Re-estimation
P
A
R
S
E
R
Grammar
s
c
o
r
e
r
correct test trees
accuracy
LEARNER
training
trees
test
sentences
cheap, plentiful
and appropriate
expensive and/or
wrong sublanguage
E step
M step

7.  Grammar re-estimation by EM method
 E step collects those expected counts
 M step sets
7

8. 8
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