NLP Language Models 1

• Information theory, IT• Entropy• Mutual Information• Use in NLP

Some basic concepts of Information Theory and Entropy

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Entropy

• Related to the coding theory- more efficient code: fewer bits for more frequent messages at the cost of more bits for the less frequent

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EXAMPLE: You have to send messages about the two occupants in a house every five minutes

• Equal probability:

0 no occupants

1 first occupant

2 second occupant

3 Both occupants

• Different probability

Situation Probability Code

no occupants .5 0

first occupant .125 110

second occupant .125 111

Both occupants .25 10

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• Let X a random variable taking values x1, x2, ..., xn from a domain de according to a probability distribution

• We can define the expected value of X, E(x) as the summatory of the possible values weighted with their probability

• E(X) = p(x1)X(x1) + p(x2)X(x2) + ... p(xn)X(xn)

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Entropy

• A message can thought of as a random variable W that can take one of several values V(W) and a probability distribution P.

• Is there a lower bound on the number of bits neede tod encode a message? Yes, the entropy

• It is possible to get close to the minimum (lower bound)

• It is also a measure of our uncertainty about wht the message says (lot of bits- uncertain, few - certain)

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• Given an event we want to associate its information content (I)

• From Shannon in the 1940s• Two constraints:

• Significance:• The less probable is an event the more information it

contains

• P(x1) > P(x2) => I(x2) > I(x1)

• Additivity:• If two events are independent

• I(x1x2) = I(x1) + I(x2)

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• I(m) = 1/p(m) does not satisfy the second requirement

• I(x) = - log p(x) satisfies both• So we define I(X) = - log p(X)

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• Let X a random variable, described by p(X), owning an information content I

• Entropy: is the expected value of I: E(I)

• Entropy measures information content of a random variable. We can consider it as the average length of the message needed to transmite a value of this variable using an optimal coding.

• Entropy measures the degree of desorder (uncertainty) of the random variable.

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• Uniform distribution of a variable X.• Each possible value xi X with |X| = M has the same

probability pi = 1/M

• If the value xi is codified in binary we need log2 M bits of information

• Non uniform distribution. • by analogy

• Each value xi has a different probability pi

• Let assume pi to be independent

• If Mpi = 1/ pi we will need log2 Mpi = log2 (1/ pi ) = - log2 pi bits of information

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X = a?

X = b?

X = c?

a

b

c a

si

si

si

no

no

no

Average number of questions: 1.75

Let X ={a, b, c, d} with pa = 1/2; pb = 1/4; pc = 1/8; pd = 1/8

entropy(X) = E(I)=-1/2 log2 (1/2) -1/4 log2 (1/4) -1/8 log2 (1/8) -1/8 log2 (1/8) = 7/4 = 1.75 bits

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Let X with a binomial distributionX = 0 with probability pX = 1 with probability (1-p)

H(X) = -p log2 (p) -(1-p) log2 (1-p)

p = 0 => 1 - p = 1 H(X) = 0p = 1 => 1 - p = 0 H(X) = 0p = 1/2 => 1 - p = 1/2 H(X) = 1

0 1/2 1 p

1

0

H(Xp)

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• joint entropy of two random variables, X, Y is average information content for specifying both variables

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• The conditional entropy of a random variable Y given another random variable X, describes what amount of information is needed in average to communicate when the reader already knows X

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P(A,B) = P(A|B)P(B) = P(B|A)P(A)

P(A,B,C,D…) = P(A)P(B|A)P(C|A,B)P(D|A,B,C..)

Chaining rule for probabilities

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Chaining rule for entropies

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I(X,Y) is the mutual information between X and Y.

• I(X,Y) measures the reduction of incertaincy of X when Y is known

• It measures too the amouny of information X owns about Y (or Y about X)

Mutual Information

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• I = 0 only when X and Y are independent:• H(X|Y)=H(X)

• H(X)=H(X)-H(X|X)=I(X,X) • Entropy is the autoinformation (mutual

information between X and X)

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• The PMI of a pair of outcomes x and y belonging to discrete random variables quantifies the discrepancy between the probability of their coincidence given their joint distribution versus the probability of their coincidence given only their individual distributions and assuming independence

• The mutual information of X and Y is the expected value of the Specific Mutual Information of all possible outcomes.

Pointwise Mutual Information

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• H: entropy of a language L• We ignore p(X)• Let q(X) a LM• How good is q(X) as an estimation of

p(X) ?

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Cross Entropy

Measures the “surprise” of a model q when it describes events following a distribution p

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Relative Entropy Relativa or Kullback-Leibler (KL) divergence

Measures the difference between two probabilistic distributions