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256 13 Text classiﬁcation and Naive Bayes

siﬁcation as discussed in Section 13.5. Section 13.6 covers evaluation of text

classiﬁcation. In the following chapters, Chapters

14 and 15, we look at two

other families of classiﬁcation methods, vector space classiﬁers and support

vector machines.

13.1 The tex t classiﬁcation problem

In text classiﬁcation, we are given a description d ∈ X of a document, where

X is the docum ent space; and a ﬁxed set of classes C = {c

, c

, . . . , c

}. ClassesDOCUMENT SPACE

CLASS

are also called ca tegories or labels. Typically, the document space X is some

type of high-dimensional space, and the classes are human deﬁned for the

needs of an application, as in the examples China and documents that talk

about multicore computer chips above. We are given a training set D of labeledTRAINING SET

documents hd, ci,where hd, ci ∈ X × C. For example:

hd, ci = hBeijing joins the World Trade Organization, Chinai

for the one-sentence document B eijing joins the World Trade Organization and

the class (or label) China.

Using a learning meth o d or learning algorithm, we then wish to learn a clas-LEARNING METHOD

siﬁer or classiﬁcation function γ that maps documents to classes:CLASSIFIER

γ : X → C

(13.1)

This type of learning is called supervised learning because a supervisor (theSUPERVISED LEARNING

human who deﬁnes the classes and labels training documents) serves as a

teacher directing the learning process. We denote the supervised learning

method by Γ and write Γ(D) = γ. The learning method Γ takes the training

set D as input and returns the learned classiﬁcation function γ.

Most names for learning methods Γ are also used for classiﬁers γ. We

talk about the Naive Bayes (NB) learning method Γ when we say that “Naive

Bayes is robust,” meaning that it can be applied to many different learning

problems and is unlikely to produce classiﬁers that fail catastrophically. But

when we say that “Naive Bayes had an error rate of 20%,” we are describing

an experiment in which a particular NB classiﬁer γ (which was produced by

the NB learning method) had a 20% error rate in an application.

Figure

13.1 shows an example of text classiﬁcation from the Reuters-RCV1

collection, introduced in Section 4.2, page 69. There are six classes (UK, Ch ina,

.. . , sports), each with three training documents. We show a few mnemonic

words for each document’s content. The training set provides some typical

examples for each class, so that we can learn the classiﬁcation function γ.

Once we have learned γ, we can apply it to the test set (or test data), for ex-TEST SET

ample, the new document ﬁrst privat e Chinese airline whose class is unknown.

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13.1 The text classiﬁcation probl em 257

classes:

training

set:

test

set:

regions

industries

subject areas

γ(d

′

) =China

ﬁrst

private

Chinese

airline

China

poultry coffee

elections

sports

London

congestion

Big Ben

Parliament

the Queen

Windsor

Beijing

Olympics

Great Wall

tourism

communist

Mao

chicken

feed

ducks

pate

turkey

bird ﬂu

beans

roasting

robusta

arabica

harvest

Kenya

votes

recount

run-off

seat

campaign

TV ads

baseball

diamond

soccer

forward

captain

team

′

◮

Figure 13.1 Classes, training set, and test set in text classiﬁcation .

In Figure 13.1, the classiﬁcation function assigns the new document to class

γ(d) = China, which is the correct assignment.

The classes in text classiﬁcation often have some interesting structure such

as the hierarchy in Figure

13.1. There are two instances each of region cate-

gories, industry categories, and subject area categories. A hierarchy can be

an important aid in solving a classiﬁcation problem; see Section

15.3.2 for

further discussion. Until then, we will make the assumption in the text clas-

siﬁcation chapters that the classes form a set with no subset relationships

between them.

Deﬁnition (

13.1) stipulates that a document is a member of exactly one

class. This is not the most appropriate model for the hierarchy in Figure 13.1.

For instance, a document about the 2008 Olympics should be a member of

two classes: the China class and the sports class. This type of classiﬁcation

problem is referred to as an any-of problem and we will return to it in Sec-

tion

14.5 (page 306). For the time being, we only consider one-of problems

where a document is a member of exactly one class.

Our goal in text classiﬁcation is high accuracy on test data or new data – for

example, the newswire articles that we will encounter tomorrow morning

in the multicore chip example. It is easy to achieve high accuracy on the

training set (e.g., we can simply memorize the labels). But high accuracy on

the training set in general does not mean that the classiﬁer will work well on

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258 13 Text classiﬁcation and Naive Bayes

new data in an application. When we use the training set to learn a classiﬁer

for test data, we make the assumption that training data and test data are

similar or from the same distribution. We defer a precise deﬁnition of this

notion to Section

14.6 (page 308).

13.2 Naive Bayes text classiﬁcation

The ﬁrst supervised learning method we introduce is the multinomial NaiveMULTINOMIAL NAIVE

BAYES

Bayes or multinomial NB model, a probabilistic learning method. The proba-

bility of a document d being in class c is computed as

P(c|d) ∝ P(c)

∏

1≤k≤n

P(t

|c)

(13.2)

where P(t

|c) is the conditional probability of term t

occurring in a docu-

ment of class c.

We interpret P(t

|c) as a measure of how much evidence

contributes that c is the correct class. P(c) is the prior probability of a

document occurring in class c. If a document’s terms do not provide clear

evidence for one class versus another, we choose the one that has a higher

prior probability. ht

, t

, . . . , t

i are the tokens in d that are part of the vocab-

ulary we use for classiﬁcation and n

is the number of such tokens in d. For

example, ht

, t

, . . . , t

i for the one-sentence document Beijing and Taipei join

the WTO might be hBeijing, Taipei, join, WTOi, with n

= 4, if we treat the terms

and and the as stop words.

In text classiﬁcation, our goal is to ﬁnd the best class for the document. The

best class in NB classiﬁcation is the most likely or maximum a posteriori (MAP)MAXIMUM A

POSTERIORI CLASS

class c

map

= arg max

c∈C

P(c|d) = arg max

c∈C

P(c)

∏

1≤k≤n

P(t

|c).

(13.3)

We write

P for P because we do not know the true values of the parameters

P(c) and P(t

|c), but estimate them from the training set as we will see in a

moment.

In Equation (

13.3), many conditional probabilities are multiplied, one for

each position 1 ≤ k ≤ n

. This can result in a ﬂoating point underﬂow.

It is therefore better to perform the computation by adding logarithms of

probabilities instead of multiplying probabilities. The class with the highest

log probability score is still the most probable; log(xy) = log(x) + log(y)

and the logarithm function is monotonic. Hence, the maximization that is

1. We will explain in the next section why P(c|d) is proportional to (∝), not equal to the quantity

on the right.

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13.2 Naive Bayes text classiﬁcation 259

actually done in most implementations of NB is:

map

= arg max

c∈C

[log

P(c) +

∑

1≤k≤n

log

P(t

|c)].

(13.4)

Equation (13.4) has a simple interpretation. Each conditional parameter

log

P(t

|c) is a weight that indicates how good an indicator t

is for c. Sim-

ilarly, the prior log

P(c) is a weight that indicates the relative frequency of

c. More frequent classes are more likely to be the correct class than infre-

quent classes. The sum of log prior and term weights is then a measure of

how much evidence there is for the document being in the class, and Equa-

tion (

13.4) selects the class for which we have the most evidence.

We will initially work with this intuitive interpretation of the multinomial

NB model and defer a formal derivation to Section

13.4.

How do we estimate the parameters

P(c) and

P(t

|c)? We ﬁrst try the

maximum likelihood estimate (MLE; Section 11.3.2, page 226), which is sim-

ply the relative frequency and corresponds to the most likely value of each

parameter given the training data. For the priors this estimate is:

P(c) =

(13.5)

where N

is the number of documents in class c and N is the total number of

documents.

We estimate the conditional probability

P(t|c) as the relative frequency of

term t in documents belonging to class c:

P(t|c) =

∑

′

∈V

′

(13.6)

where T

is the number of occurrences of t in training documents from class

c, including multiple occurrences of a term in a document. We have made the

positional independence assumption here, which we will discuss in more detail

in the next section: T

is a count of occurrences in all positions k in the doc-

uments in the training set. Thus, we do not compute different estimates for

different positions and, for example, if a word occurs twice in a document,

in positions k

and k

, then

P(t

|c) =

P(t

|c).

The problem with the MLE estimate is that it is zero for a term–class combi-

nation that did not occur in the training data. If the term WTO in the training

data only occurred in China documents, then the MLE estimates for the other

classes, for example UK, will be zero:

P(WTO|UK) = 0.

Now, the one-sentence document Britain is a member of the WTO will get a

conditional probability of zero for UK because we are multiplying the condi-

tional probabilities for all terms in Equation (

13.2). Clearly, the model should

Online edition (c)2009 Cambridge UP

260 13 Text classiﬁcation and Naive Bayes

TRAINMULTINOMIALNB(C, D)

1 V ← EXTRACTVOCABULARY(D)

2 N ← COUNTDOCS(D)

3 for each c ∈ C

4 do N

← COUNTDOCSINCLASS(D, c)

5 prior[c] ← N

6 text

← CONCATENATETEXTOFALLDOCSINCLASS(D, c)

7 for each t ∈ V

8 do T

← COUNTTOKENSOFTERM(text

, t)

9 for each t ∈ V

10 do condprob[t][c] ←

∑

′

+1)

11 return V, prior, condprob

APPLYMULTINOMIALNB(C, V, prior, condprob, d)

1 W ← EXTRACTTOKENSFROMDOC(V, d)

2 for each c ∈ C

3 do score[c] ← log prior[c]

4 for each t ∈ W

5 do score[c] += log condprob[t][c]

6 return arg max

c∈C

score[c]

◮

Figure 13.2 Naive Bayes algorithm (multinomial model): Training and testing.

assign a high probability to the UK class because the term Britain occurs. The

problem is that the zero probability for WTO cannot be “conditioned away,”

no matter how strong the evidence for the class UK from other features. The

estimate is 0 because of sparseness: The training data are never large enoughSPARSENESS

to represent the frequency of rare events adequately, for example, the fre-

quency of WTO occurring in UK documents.

To eliminate zeros, we use add-one or Laplace smoothing, which simply addsADD-ONE SMOOTHING

one to each count (cf. Section

11.3.2):

P(t|c) =

+ 1

∑

′

∈V

′ + 1)

+ 1

(

∑

′

∈V

′) + B

(13.7)

where B = |V| is the number of terms in the vocabulary. Add-one smoothing

can be interpreted as a uniform prior (each term occurs once for each class)

that is then updated as evidence from the training data comes in. Note that

this is a prior probability for the occurrence of a term as opposed to the prior

probability of a class which we estimate in Equation (

13.5) on the document

level.

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13.2 Naive Bayes text classiﬁcation 261

◮

Table 13.1 Data for parameter estimation examples.

docID words in document in c = China?

training set 1 Chinese Beijing Chinese yes

2 Chinese Chinese Shanghai yes

3 Chinese Macao yes

4 Tokyo Japan Chinese no

test set 5 Chinese Chinese Chinese Tokyo Japan ?

◮

Table 13.2 Training and test times for NB.

mode time complexity

training Θ(|D|L

ave

+ |C||V|)

testing Θ(L

+ |C|M

) = Θ(|C|M

)

We have now introduced all the elements we need for training and apply-

ing an NB classiﬁer. The complete algorithm is described in Figure

13.2.

✎

Example 13.1: For the example in Table

13.1, the multinomial parameters we

need to classify the test document are the priors

P(c) = 3/4 and

c) = 1/4 and the

following conditional probabilities:

P(Chinese|c) = (5 + 1)/(8 + 6) = 6/14 = 3/7

P(Tokyo|c) =

P(Japan|c) = (0 + 1)/(8 + 6) = 1/14

P(Chinese|

c) = (1 + 1)/(3 + 6) = 2/9

P(Tokyo|

c) =

P(Japan|c) = (1 + 1)/(3 + 6) = 2/9

The denominators are (8 + 6) and (3 + 6) because the lengths of text

and text

are 8

and 3, respectively, and because the constant B in Equation (

13.7) is 6 as the vocabu-

lary consists of six terms.

We then get:

P(c|d

) ∝ 3/4 ·(3/7)

·1/14 ·1/14 ≈ 0.0003.

P(c|d

) ∝ 1/4 ·(2/9)

·2/9 ·2/9 ≈ 0.0001.

Thus, the classiﬁer assigns the test document to c = China. The reason for this clas-

siﬁcation decision is that the three occurrences of the positive indicator Chinese in d

outweigh the occurrences of the two negative indicators Japan and Tokyo.

What is the time complexity of NB? The complexity of computing the pa-

rameters is Θ(|C||V|) because the set of parameters consists of |C||V| con-

ditional probabilities and |C| priors. The preprocessing necessary for com-

puting the parameters (extracting the vocabulary, counting terms, etc.) can

be done in one pass through the training data. The time complexity of this

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262 13 Text classiﬁcation and Naive Bayes

component is therefore Θ(|D|L

ave

), where |D| is the number of documents

and L

ave

is the average length of a document.

We use Θ(|D|L

ave

) as a notation for Θ(T) here, where T is the length of the

training collection. This is nonstandard; Θ(.) is not deﬁned for an average.

We prefer expressing the time complexity in terms of D and L

ave

because

these are the primary statistics used to characterize training collections.

The time complexity of APPLYMULTINOMIALNB in Figure

13.2 is Θ(|C|L

and M

are the numbers of tokens and types, respectively, in the test doc-

ument. APPLYMULTINOMIALNB can be modiﬁed to be Θ(L

+ |C|M

) (Ex-

ercise

13.8). Finally, assuming that the length of test documents is bounded,

Θ(L

+ |C|M

) = Θ(|C|M

) because L

< b|C|M

for a ﬁxed constant b.

Table

13.2 summarizes the time complexities. In general, we have |C||V| <

|D|L

ave

, so both training and testing complexity are linear in the time it takes

to scan the data. Because we have to look at the data at least once, NB can be

said to have optimal time complexity. Its efﬁciency is one reason why NB is

a popular text classiﬁcation method.

13.2.1 R elation to multinomial unigram language model

The multinomial NB model is formally identical to the multinomial unigram

language model (Section

12.2.1, page 242). In particular, Equation (13.2) is

a special case of Equation (

12.12) from page 243, which we repeat here for

λ = 1:

P(d|q) ∝ P(d)

∏

t∈q

P(t|M

(13.8)

The document d in text classiﬁcation (Equation (13.2)) takes the role of the

query in language modeling (Equation (13.8)) and the classes c in text clas-

siﬁcation take the role of the documents d in language modeling. We used

Equation (

13.8) to rank documents according to the probability that they are

relevant to the query q . In NB classiﬁcation, we are usually only interested

in the top-ranked class.

We also used MLE estimates in Section

12.2.2 (page 243) and encountered

the problem of zero estimates owing to sparse data (page 244); but instead

of add-one smoothing, we used a mixture of two distributions to address the

problem there. Add-one smoothing is closely related to add-

smoothing in

Section

11.3.4 (page 228).

Exercise 13.1

Why is |C||V| < |D|L

ave

in Table 13.2 expected to hold for most text collections?

2. Our assumption here is that the length of test documents is bounded. L

would exceed

b|C|M

for extremely long test documents.

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13.3 The Bernoulli model 263

TRAINBERNOULLINB(C, D)

1 V ← EXTRACTVOCABULARY(D)

2 N ← COUNTDOCS(D)

3 for each c ∈ C

4 do N

← COUNTDOCSINCLASS(D, c)

5 pr io r[c] ← N

6 for each t ∈ V

7 do N

← COUNTDOCSINCLASSCONTAININGTERM(D, c, t)

8 condprob[t][c] ← (N

+ 1)/(N

+ 2)

9 return V, prior, condp rob

APPLYBERNOULLINB(C, V, prior, condprob, d)

1 V

← EXTRACTTERMSFROMDOC(V, d)

2 for each c ∈ C

3 do score[c] ← log prior[c]

4 for each t ∈ V

5 do if t ∈ V

6 then score[c] += log condprob[t][c]

7 else score[c] += log(1 − condprob[t][c])

8 return arg max

c∈C

score[c]

◮

Figure 13.3 NB algorithm (Bernoulli model): Training and testing. The add-one

smoothing in Line 8 (top) is in analogy to Equation (13.7) with B = 2.

13.3 The Bernoulli m odel

There are two different ways we can set up an NB classiﬁer. The model we in-

troduced in the previous section is the multinomial model. It generates one

term from the vocabulary in each position of the document, where we as-

sume a generative model that will be discussed in more detail in Section

13.4

(see also page 237).

An alternative to the multinomial model is the multivariate Bernoulli model

or Bernoulli model. It is equivalent to the binary independence model of Sec-BERNOULLI MODEL

tion

11.3 (page 222), which generates an indicator for each term of the vo-

cabulary, either 1 indicating presence of the term in the document or 0 indi-

cating absence. Figure 13.3 presents training and testing algorithms for the

Bernoulli model. The Bernoulli model has the same time complexity as the

multinomial model.

The different generation models imply different estimation strategies and

different classiﬁcation rules. The Bernoulli model estimates

P(t|c) as the frac-

tion of d o cuments of class c that contain term t (Figure

13.3, TRAINBERNOULLI-

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264 13 Text classiﬁcation and Naive Bayes

NB, line 8). In contrast, the multinomial model estimates

P(t|c) as the frac-

tion of tokens or fraction of positions in documents of class c that contain term

t (Equation (

13.7)). When classifying a test document, the Bernoulli model

uses binary occurrence information, ignoring the number of occurrences,

whereas the multinomial model keeps track of multiple occurrences. As a

result, the Bernoulli model typically makes many mistakes when classifying

long documents. For example, it may assign an entire book to the class China

because of a single occurrence of the term China.

The models also differ in how nonoccurring terms are used in classiﬁca-

tion. They do not affect the classiﬁcation decision in the multinomial model;

but in the Bernoulli model the probability of nonoccurrence is factored in

when computing P(c|d) (Figure

13.3, APPLYBERNOULLINB, Line 7). This is

because only the Bernoulli NB model models absence of terms explicitly.

✎

Example 13.2: Applying the Bernoulli model to the example in Table

13.1, we

have the same estimates for the priors as before:

P(c) = 3/4,

c) = 1/4. The

conditional probabilities are:

P(Chinese|c) = (3 + 1)/(3 + 2) = 4/5

P(Japan|c) =

P(Tokyo|c) = (0 + 1)/(3 + 2) = 1/5

P(Beijing|c) =

P(Macao|c) =

P(Shanghai|c) = (1 + 1)/(3 + 2) = 2/5

P(Chinese|

c) = (1 + 1)/(1 + 2) = 2/3

P(Japan|

c) =

P(Tokyo|c) = (1 + 1)/(1 + 2) = 2/3

P(Beijing|

c) =

P(Macao|c) =

P(Shanghai|c) = (0 + 1)/(1 + 2) = 1/3

The denominators are (3 + 2) and (1 + 2) because there are three documents in c

and one document in

c and because the constant B in Equation (13.7) is 2 – there are

two cases to consider for each term, occurrence and nonoccurrence.

The scores of the test document for the two classes are

P(c|d

) ∝

P(c) ·

P(Chinese|c) ·

P(Japan|c) ·

P(Tokyo|c)

·(1 −

P(Beijing|c)) · (1 −

P(Shanghai|c)) · (1 −

P(Macao|c))

= 3/4 ·4/5 ·1/5 ·1/5 · (1−2/5) ·(1−2/5) · (1−2/5)

≈ 0.005

and, analogously,

c|d

) ∝ 1/4 ·2/3 ·2/3 ·2/3 ·(1−1/3) · (1−1/3) · (1−1/3)

≈ 0.022

Thus, the classiﬁer assigns the test document to c = not-China. When looking only

at binary occurrence and not at term frequency, Japan and Tokyo are indicators for

(2/3 > 1/5) and the conditional probabilities of Chinese for c and

c are not different

enough (4/5 vs. 2/3) to affect the classiﬁcation decision.

Online edition (c)2009 Cambridge UP

13.4 Properties of Naive Bayes 265

13.4 Properties of Naive Bayes

To gain a better understanding of the two models and the assumptions they

make, let us go back and examine how we derived their classiﬁcation rules in

Chapters

11 and 12. We decide class membership of a document by assigning

it to the class with the maximum a posteriori probability (cf. Section

11.3.2,

page 226), which we compute as follows:

map

= arg max

c∈C

P(c|d)

= arg max

c∈C

P(d|c)P(c)

P(d)

(13.9)

= arg max

c∈C

P(d|c)P(c),(13.10)

where Bayes’ rule (Equation (11.4), page 220) is applied in (13.9) and we drop

the denominator in the last step because P(d) is the same for all classes and

does not affect the argmax.

We can interpret Equation (

13.10) as a description of the generative process

we assume in Bayesian text classiﬁcation. To generate a document, we ﬁrst

choose class c with probability P(c) (top nodes in Figures

13.4 and 13.5). The

two models differ in the formalization of the second step, the generation of

the document given the class, corresponding to the conditional distribution

P(d|c):

Multinomial P(d|c) = P(ht

, . . . , t

i|c)(13.11)

Bernoulli P(d|c) = P(he

, . . . , e

i|c),(13.12)

where ht

, . . . , t

i is the sequence of terms as it occurs in d (minus terms

that were excluded from the vocabulary) and he

, . . . , e

i is a binary

vector of dimensionality M that indicates for each term whether it occurs in

d or not.

It should now be clearer why we introduced the document space X in

Equation (

13.1) when we deﬁned the classiﬁcation problem. A critical step

in solving a text classiﬁcation problem is to choose the document represen-

tation. ht

, . . . , t

i and he

, . . . , e

i are two different document representa-

tions. In the ﬁrst case, X is the set of all term sequences (or, more precisely,

sequences of term tokens). In the second case, X is {0, 1}

We cannot use Equations (

13.11) and (13.12) for text classiﬁcation directly.

For the Bernoulli model, we would have to estimate 2

|C| different param-

eters, one for each possible combination of M values e

and a class. The

number of parameters in the multinomial case has the same order of magni-