Online edition (c) 2009 Cambridge UP
Online edition (c) 2009 Cambridge UP
DRAFT! © April 1, 2009 Cambridge University Press. Feedback welcome. 319
15 Support vector machines and
machine learning on documents
Improving classifier effectiveness has been an area of intensive machinelearning
research over the last two decades, and this work has led to a new
generation of state-of-the-art classifiers, such as support vector machines,
boosted decision trees, regularized logistic regression, neural networks, and
random forests. Many of these methods, including support vector machines
(SVMs), the main topic of this chapter, have been applied with success to
information retrieval problems, particularly text classification. An SVM is a
kind of large-margin classifier: it is a vector space based machine learning
method where the goal is to find a decision boundary between two classes
that is maximally far from any point in the training data (possibly discounting
some points as outliers or noise).
We will initially motivate and develop SVMs for the case of two-class data
sets that are separable by a linear classifier (Section 15.1), and then extend the
model in Section 15.2 to non-separable data, multi-class problems, and nonlinear
models, and also present some additional discussion of SVM performance.
The chapter then moves to consider the practical deployment of text
classifiers in Section 15.3: what sorts of classifiers are appropriate when, and
how can you exploit domain-specific text features in classification? Finally,
we will consider how the machine learning technology that we have been
building for text classification can be applied back to the problem of learning
how to rank documents in ad hoc retrieval (Section 15.4). While several machine
learning methods have been applied to this task, use of SVMs has been
prominent. Support vector machines are not necessarily better than other
machine learning methods (except perhaps in situations with little training
data), but they perform at the state-of-the-art level and have much current
theoretical and empirical appeal.
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320 15 Support vector machines and machine learning on documents
Support vectorsMaximum
margin
decision
hyperplane
Margin is
maximized
◮ Figure 15.1 The support vectors are the 5 points right up against the margin of
the classifier.
15.1 Support vector machines: The linearly separable case
For two-class, separable training data sets, such as the one in Figure 14.8
(page 301), there are lots of possible linear separators. Intuitively, a decision
boundary drawn in the middle of the void between data items of the two
classes seems better than one which approaches very close to examples of
one or both classes. While some learning methods such as the perceptron
algorithm (see references in Section 14.7, page 314) find just any linear separator,
others, like Naive Bayes, search for the best linear separator according
to some criterion. The SVM in particular defines the criterion to be looking
for a decision surface that is maximally far away from any data point. This
distance from the decision surface to the closest data point determines the
margin of the classifier. This method of construction necessarily means thatMARGIN
the decision function for an SVM is fully specified by a (usually small) subset
of the data which defines the position of the separator. These points are
referred to as the support vectors (in a vector space, a point can be thought ofSUPPORT VECTOR
as a vector between the origin and that point). Figure 15.1 shows the margin
and support vectors for a sample problem. Other data points play no part in
determining the decision surface that is chosen.
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15.1 Support vector machines: The linearly separable case 321
◮ Figure 15.2 An intuition for large-margin classification. Insisting on a large margin
reduces the capacity of the model: the range of angles at which the fat decision
surface can be placed is smaller than for a decision hyperplane (cf. Figure 14.8,
page 301).
Maximizing the margin seems good because points near the decision surface
represent very uncertain classification decisions: there is almost a 50%
chance of the classifier deciding either way. A classifier with a large margin
makes no low certainty classification decisions. This gives you a classification
safety margin: a slight error in measurement or a slight document variation
will not cause a misclassification. Another intuition motivating SVMs
is shown in Figure 15.2. By construction, an SVM classifier insists on a large
margin around the decision boundary. Compared to a decision hyperplane,
if you have to place a fat separator between classes, you have fewer choices
of where it can be put. As a result of this, the memory capacity of the model
has been decreased, and hence we expect that its ability to correctly generalize
to test data is increased (cf. the discussion of the bias-variance tradeoff in
Chapter 14, page 312).
Let us formalize an SVM with algebra. A decision hyperplane (page 302)
can be defined by an intercept term b and a decision hyperplane normal vector
w which is perpendicular to the hyperplane. This vector is commonly
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322 15 Support vector machines and machine learning on documents
referred to in the machine learning literature as the weight vector. To chooseWEIGHT VECTOR
among all the hyperplanes that are perpendicular to the normal vector, we
specify the intercept term b. Because the hyperplane is perpendicular to the
normal vector, all points x on the hyperplane satisfy wT
x = −b. Now suppose
that we have a set of training data points D = {(xi, yi)}, where each
member is a pair of a point xi and a class label yi corresponding to it.1 For
SVMs, the two data classes are always named +1 and −1 (rather than 1 and
0), and the intercept term is always explicitly represented as b (rather than
being folded into the weight vector w by adding an extra always-on feature).
The math works out much more cleanly if you do things this way, as we will
see almost immediately in the definition of functional margin. The linear
classifier is then:
f (x) = sign(wT
x + b)(15.1)
A value of −1 indicates one class, and a value of +1 the other class.
We are confident in the classification of a point if it is far away from the
decision boundary. For a given data set and decision hyperplane, we define
the functional margin of the ith
example xi with respect to a hyperplane w, bFUNCTIONAL MARGIN
as the quantity yi(wT
xi + b). The functional margin of a data set with respect
to a decision surface is then twice the functional margin of any of the
points in the data set with minimal functional margin (the factor of 2 comes
from measuring across the whole width of the margin, as in Figure 15.3).
However, there is a problem with using this definition as is: the value is underconstrained,
because we can always make the functional margin as big
as we wish by simply scaling up w and b. For example, if we replace w by
5w and b by 5b then the functional margin yi(5wT
xi + 5b) is five times as
large. This suggests that we need to place some constraint on the size of the
w vector. To get a sense of how to do that, let us look at the actual geometry.
What is the Euclidean distance from a point x to the decision boundary? In
Figure 15.3, we denote by r this distance. We know that the shortest distance
between a point and a hyperplane is perpendicular to the plane, and hence,
parallel to w. A unit vector in this direction is w/|w|. The dotted line in the
diagram is then a translation of the vector rw/|w|. Let us label the point on
the hyperplane closest to x as x′. Then:
x′
= x − yr
w
|w|
(15.2)
where multiplying by y just changes the sign for the two cases of x being on
either side of the decision surface. Moreover, x′ lies on the decision boundary
1. As discussed in Section 14.1 (page 291), we present the general case of points in a vector
space, but if the points are length normalized document vectors, then all the action is taking
place on the surface of a unit sphere, and the decision surface intersects the sphere’s surface.
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15.1 Support vector machines: The linearly separable case 323
0 1 2 3 4 5 6 7 8
0
1
2
3
4
5
6
7
x
+
x′
r
ρ
w
◮ Figure 15.3 The geometric margin of a point (r) and a decision boundary (ρ).
and so satisfies wT
x′ + b = 0. Hence:
wT
x − yr
w
|w|
+ b = 0(15.3)
Solving for r gives:2
r = y
wT
x + b
|w|
(15.4)
Again, the points closest to the separating hyperplane are support vectors.
The geometric margin of the classifier is the maximum width of the band thatGEOMETRIC MARGIN
can be drawn separating the support vectors of the two classes. That is, it is
twice the minimum value over data points for r given in Equation (15.4), or,
equivalently, the maximal width of one of the fat separators shown in Figure
15.2. The geometric margin is clearly invariant to scaling of parameters:
if we replace w by 5w and b by 5b, then the geometric margin is the same, because
it is inherently normalized by the length of w. This means that we can
impose any scaling constraint we wish on w without affecting the geometric
margin. Among other choices, we could use unit vectors, as in Chapter 6, by
2. Recall that |w| =
√
wTw.
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324 15 Support vector machines and machine learning on documents
requiring that |w| = 1. This would have the effect of making the geometric
margin the same as the functional margin.
Since we can scale the functional margin as we please, for convenience in
solving large SVMs, let us choose to require that the functional margin of all
data points is at least 1 and that it is equal to 1 for at least one data vector.
That is, for all items in the data:
yi(wT
xi + b) ≥ 1(15.5)
and there exist support vectors for which the inequality is an equality. Since
each example’s distance from the hyperplane is ri = yi(wT
xi + b)/|w|, the
geometric margin is ρ = 2/|w|. Our desire is still to maximize this geometric
margin. That is, we want to find w and b such that:
• ρ = 2/|w| is maximized
• For all (xi, yi) ∈ D, yi(wT
xi + b) ≥ 1
Maximizing 2/|w| is the same as minimizing |w|/2. This gives the final standard
formulation of an SVM as a minimization problem:
(15.6) Find w and b such that:
• 1
2 wT
w is minimized, and
• for all {(xi, yi)}, yi(wT
xi + b) ≥ 1
We are now optimizing a quadratic function subject to linear constraints.
Quadratic optimization problems are a standard, well-known class of mathe-QUADRATIC
PROGRAMMING matical optimization problems, and many algorithms exist for solving them.
We could in principle build our SVM using standard quadratic programming
(QP) libraries, but there has been much recent research in this area aiming to
exploit the structure of the kind of QP that emerges from an SVM. As a result,
there are more intricate but much faster and more scalable libraries available
especially for building SVMs, which almost everyone uses to build models.
We will not present the details of such algorithms here.
However, it will be helpful to what follows to understand the shape of the
solution of such an optimization problem. The solution involves constructing
a dual problem where a Lagrange multiplier αi is associated with each
constraint yi(wT
xi + b) ≥ 1 in the primal problem:
(15.7) Find α1, . . . αN such that ∑ αi − 1
2 ∑i ∑j αiαjyiyjxi
T
xj is maximized, and
• ∑i αiyi = 0
• αi ≥ 0 for all 1 ≤ i ≤ N
The solution is then of the form:
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15.1 Support vector machines: The linearly separable case 325
0 1 2 3
0
1
2
3
◮ Figure 15.4 A tiny 3 data point training set for an SVM.
(15.8) w = ∑ αiyixi
b = yk − wT
xk for any xk such that αk = 0
In the solution, most of the αi are zero. Each non-zero αi indicates that the
corresponding xi is a support vector. The classification function is then:
f (x) = sign(∑i
αiyixi
T
x + b)(15.9)
Both the term to be maximized in the dual problem and the classifying function
involve a dot product between pairs of points (x and xi or xi and xj), and
that is the only way the data are used – we will return to the significance of
this later.
To recap, we start with a training data set. The data set uniquely defines
the best separating hyperplane, and we feed the data through a quadratic
optimization procedure to find this plane. Given a new point x to classify,
the classification function f (x) in either Equation (15.1) or Equation (15.9) is
computing the projection of the point onto the hyperplane normal. The sign
of this function determines the class to assign to the point. If the point is
within the margin of the classifier (or another confidence threshold t that we
might have determined to minimize classification mistakes) then the classifier
can return “don’t know” rather than one of the two classes. The value
of f (x) may also be transformed into a probability of classification; fitting
a sigmoid to transform the values is standard (Platt 2000). Also, since the
margin is constant, if the model includes dimensions from various sources,
careful rescaling of some dimensions may be required. However, this is not
a problem if our documents (points) are on the unit hypersphere.
 Example 15.1: Consider building an SVM over the (very little) data set shown in
Figure 15.4. Working geometrically, for an example like this, the maximum margin
weight vector will be parallel to the shortest line connecting points of the two classes,
that is, the line between (1, 1) and (2, 3), giving a weight vector of (1, 2). The optimal
decision surface is orthogonal to that line and intersects it at the halfway point.
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326 15 Support vector machines and machine learning on documents
Therefore, it passes through (1.5, 2). So, the SVM decision boundary is:
y = x1 + 2x2 − 5.5
Working algebraically, with the standard constraint that sign(yi(wTxi + b)) ≥ 1,
we seek to minimize |w|. This happens when this constraint is satisfied with equality
by the two support vectors. Further we know that the solution is w = (a, 2a) for some
a. So we have that:
a + 2a + b = −1
2a + 6a + b = 1
Therefore, a = 2/5 and b = −11/5. So the optimal hyperplane is given by w =
(2/5, 4/5) and b = −11/5.
The margin ρ is 2/|w| = 2/
√
4/25 + 16/25 = 2/(2
√
5/5) =
√
5. This answer can
be confirmed geometrically by examining Figure 15.4.
?
Exercise 15.1 [⋆]
What is the minimum number of support vectors that there can be for a data set
(which contains instances of each class)?
Exercise 15.2 [⋆⋆]
The basis of being able to use kernels in SVMs (see Section 15.2.3) is that the classification
function can be written in the form of Equation (15.9) (where, for large problems,
most αi are 0). Show explicitly how the classification function could be written in this
form for the data set from Example 15.1. That is, write f as a function where the data
points appear and the only variable is x.
Exercise 15.3 [⋆⋆]
Install an SVM package such as SVMlight (http://svmlight.joachims.org/), and build an
SVM for the data set discussed in Example 15.1. Confirm that the program gives the
same solution as the text. For SVMlight, or another package that accepts the same
training data format, the training file would be:
+1 1:2 2:3
−1 1:2 2:0
−1 1:1 2:1
The training command for SVMlight is then:
svm_learn -c 1 -a alphas.dat train.dat model.dat
The -c 1 option is needed to turn off use of the slack variables that we discuss in
Section 15.2.1. Check that the norm of the weight vector agrees with what we found
in Example 15.1. Examine the file alphas.dat which contains the αi values, and check
that they agree with your answers in Exercise 15.2.
Online edition (c) 2009 Cambridge UP
15.2 Extensions to the SVM model 327
xi
ξi
xj
ξj
◮ Figure 15.5 Large margin classification with slack variables.
15.2 Extensions to the SVM model
15.2.1 Soft margin classification
For the very high dimensional problems common in text classification, sometimes
the data are linearly separable. But in the general case they are not, and
even if they are, we might prefer a solution that better separates the bulk of
the data while ignoring a few weird noise documents.
If the training set D is not linearly separable, the standard approach is to
allow the fat decision margin to make a few mistakes (some points – outliers
or noisy examples – are inside or on the wrong side of the margin). We then
pay a cost for each misclassified example, which depends on how far it is
from meeting the margin requirement given in Equation (15.5). To implement
this, we introduce slack variables ξi. A non-zero value for ξi allows xi toSLACK VARIABLES
not meet the margin requirement at a cost proportional to the value of ξi. See
Figure 15.5.
The formulation of the SVM optimization problem with slack variables is:
(15.10) Find w, b, and ξi ≥ 0 such that:
• 1
2 wT
w + C ∑i ξi is minimized
• and for all {(xi, yi)}, yi(wT
xi + b) ≥ 1 − ξi
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328 15 Support vector machines and machine learning on documents
The optimization problem is then trading off how fat it can make the margin
versus how many points have to be moved around to allow this margin.
The margin can be less than 1 for a point xi by setting ξi > 0, but then one
pays a penalty of Cξi in the minimization for having done that. The sum of
the ξi gives an upper bound on the number of training errors. Soft-margin
SVMs minimize training error traded off against margin. The parameter C
is a regularization term, which provides a way to control overfitting: as CREGULARIZATION
becomes large, it is unattractive to not respect the data at the cost of reducing
the geometric margin; when it is small, it is easy to account for some data
points with the use of slack variables and to have a fat margin placed so it
models the bulk of the data.
The dual problem for soft margin classification becomes:
(15.11) Find α1, . . . αN such that ∑ αi − 1
2 ∑i ∑j αiαjyiyjxi
T
xj is maximized, and
• ∑i αiyi = 0
• 0 ≤ αi ≤ C for all 1 ≤ i ≤ N
Neither the slack variables ξi nor Lagrange multipliers for them appear in the
dual problem. All we are left with is the constant C bounding the possible
size of the Lagrange multipliers for the support vector data points. As before,
the xi with non-zero αi will be the support vectors. The solution of the dual
problem is of the form:
(15.12) w = ∑ αyixi
b = yk(1 − ξk) − wT
xk for k = arg maxk αk
Again w is not needed explicitly for classification, which can be done in terms
of dot products with data points, as in Equation (15.9).
Typically, the support vectors will be a small proportion of the training
data. However, if the problem is non-separable or with small margin, then
every data point which is misclassified or within the margin will have a nonzero
αi. If this set of points becomes large, then, for the nonlinear case which
we turn to in Section 15.2.3, this can be a major slowdown for using SVMs at
test time.
The complexity of training and testing with linear SVMs is shown in Table
15.1.3 The time for training an SVM is dominated by the time for solving
the underlying QP, and so the theoretical and empirical complexity varies depending
on the method used to solve it. The standard result for solving QPs
is that it takes time cubic in the size of the data set (Kozlov et al. 1979). All the
recent work on SVM training has worked to reduce that complexity, often by
3. We write Θ(|D|Lave) for Θ(T) (page 262) and assume that the length of test documents is
bounded as we did on page 262.
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15.2 Extensions to the SVM model 329
Classifier Mode Method Time complexity
NB training Θ(|D|Lave + |C||V|)
NB testing Θ(|C|Ma)
Rocchio training Θ(|D|Lave + |C||V|)
Rocchio testing Θ(|C|Ma)
kNN training preprocessing Θ(|D|Lave)
kNN testing preprocessing Θ(|D|Mave Ma)
kNN training no preprocessing Θ(1)
kNN testing no preprocessing Θ(|D|Lave Ma)
SVM training conventional O(|C||D|3Mave);
≈ O(|C||D|1.7Mave), empirically
SVM training cutting planes O(|C||D|Mave)
SVM testing O(|C|Ma)
◮ Table 15.1 Training and testing complexity of various classifiers including SVMs.
Training is the time the learning method takes to learn a classifier over D, while testing
is the time it takes a classifier to classify one document. For SVMs, multiclass
classification is assumed to be done by a set of |C| one-versus-rest classifiers. Lave is
the average number of tokens per document, while Mave is the average vocabulary
(number of non-zero features) of a document. La and Ma are the numbers of tokens
and types, respectively, in the test document.
being satisfied with approximate solutions. Standardly, empirical complexity
is about O(|D|1.7) (Joachims 2006a). Nevertheless, the super-linear training
time of traditional SVM algorithms makes them difficult or impossible
to use on very large training data sets. Alternative traditional SVM solution
algorithms which are linear in the number of training examples scale
badly with a large number of features, which is another standard attribute
of text problems. However, a new training algorithm based on cutting plane
techniques gives a promising answer to this issue by having running time
linear in the number of training examples and the number of non-zero features
in examples (Joachims 2006a). Nevertheless, the actual speed of doing
quadratic optimization remains much slower than simply counting terms as
is done in a Naive Bayes model. Extending SVM algorithms to nonlinear
SVMs, as in the next section, standardly increases training complexity by a
factor of |D| (since dot products between examples need to be calculated),
making them impractical. In practice it can often be cheaper to materialize
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330 15 Support vector machines and machine learning on documents
the higher-order features and to train a linear SVM.4
15.2.2 Multiclass SVMs
SVMs are inherently two-class classifiers. The traditional way to do multiclass
classification with SVMs is to use one of the methods discussed in
Section 14.5 (page 306). In particular, the most common technique in practice
has been to build |C| one-versus-rest classifiers (commonly referred to as
“one-versus-all” or OVA classification), and to choose the class which classifies
the test datum with greatest margin. Another strategy is to build a set
of one-versus-one classifiers, and to choose the class that is selected by the
most classifiers. While this involves building |C|(|C| − 1)/2 classifiers, the
time for training classifiers may actually decrease, since the training data set
for each classifier is much smaller.
However, these are not very elegant approaches to solving multiclass problems.
A better alternative is provided by the construction of multiclass SVMs,
where we build a two-class classifier over a feature vector Φ(x, y) derived
from the pair consisting of the input features and the class of the datum. At
test time, the classifier chooses the class y = arg maxy′ wT
Φ(x, y′). The margin
during training is the gap between this value for the correct class and
for the nearest other class, and so the quadratic program formulation will
require that ∀i ∀y = yi wT
Φ(xi, yi) − wT
Φ(xi, y) ≥ 1 − ξi. This general
method can be extended to give a multiclass formulation of various kinds of
linear classifiers. It is also a simple instance of a generalization of classification
where the classes are not just a set of independent, categorical labels, but
may be arbitrary structured objects with relationships defined between them.
In the SVM world, such work comes under the label of structural SVMs. WeSTRUCTURAL SVMS
mention them again in Section 15.4.2.
15.2.3 Nonlinear SVMs
With what we have presented so far, data sets that are linearly separable (perhaps
with a few exceptions or some noise) are well-handled. But what are
we going to do if the data set just doesn’t allow classification by a linear classifier?
Let us look at a one-dimensional case. The top data set in Figure 15.6
is straightforwardly classified by a linear classifier but the middle data set is
not. We instead need to be able to pick out an interval. One way to solve this
problem is to map the data on to a higher dimensional space and then to use
a linear classifier in the higher dimensional space. For example, the bottom
part of the figure shows that a linear separator can easily classify the data
4. Materializing the features refers to directly calculating higher order and interaction terms
and then putting them into a linear model.
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15.2 Extensions to the SVM model 331
◮ Figure 15.6 Projecting data that is not linearly separable into a higher dimensional
space can make it linearly separable.
if we use a quadratic function to map the data into two dimensions (a polar
coordinates projection would be another possibility). The general idea is
to map the original feature space to some higher-dimensional feature space
where the training set is separable. Of course, we would want to do so in
ways that preserve relevant dimensions of relatedness between data points,
so that the resultant classifier should still generalize well.
SVMs, and also a number of other linear classifiers, provide an easy and
efficient way of doing this mapping to a higher dimensional space, which is
referred to as “the kernel trick”. It’s not really a trick: it just exploits the mathKERNEL TRICK
that we have seen. The SVM linear classifier relies on a dot product between
data point vectors. Let K(xi, xj) = xi
T
xj. Then the classifier we have seen so
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332 15 Support vector machines and machine learning on documents
far is:
f (x) = sign(∑
i
αiyiK(xi, x) + b)(15.13)
Now suppose we decide to map every data point into a higher dimensional
space via some transformation Φ: x → φ(x). Then the dot product becomes
φ(xi)T
φ(xj). If it turned out that this dot product (which is just a real number)
could be computed simply and efficiently in terms of the original data
points, then we wouldn’t have to actually map from x → φ(x). Rather, we
could simply compute the quantity K(xi, xj) = φ(xi)T
φ(xj), and then use the
function’s value in Equation (15.13). A kernel function K is such a functionKERNEL FUNCTION
that corresponds to a dot product in some expanded feature space.
 Example 15.2: The quadratic kernel in two dimensions. For 2-dimensional
vectors u = (u1 u2), v = (v1 v2), consider K(u, v) = (1 + uTv)2. We wish to
show that this is a kernel, i.e., that K(u, v) = φ(u)Tφ(v) for some φ. Consider φ(u) =
(1 u2
1
√
2u1u2 u2
2
√
2u1
√
2u2). Then:
K(u, v) = (1 + uTv)2
(15.14)
= 1 + u2
1v2
1 + 2u1v1u2v2 + u2
2v2
2 + 2u1v1 + 2u2v2
= (1 u2
1
√
2u1u2 u2
2
√
2u1
√
2u2)T(1 v2
1
√
2v1v2 v2
2
√
2v1
√
2v2)
= φ(u)Tφ(v)
In the language of functional analysis, what kinds of functions are valid
kernel functions? Kernel functions are sometimes more precisely referred toKERNEL
as Mercer kernels, because they must satisfy Mercer’s condition: for any g(x)MERCER KERNEL
such that g(x)2dx is finite, we must have that:
K(x, z)g(x)g(z)dxdz ≥ 0 .(15.15)
A kernel function K must be continuous, symmetric, and have a positive definite
gram matrix. Such a K means that there exists a mapping to a reproducing
kernel Hilbert space (a Hilbert space is a vector space closed under dot
products) such that the dot product there gives the same value as the function
K. If a kernel does not satisfy Mercer’s condition, then the corresponding QP
may have no solution. If you would like to better understand these issues,
you should consult the books on SVMs mentioned in Section 15.5. Otherwise,
you can content yourself with knowing that 90% of work with kernels
uses one of two straightforward families of functions of two vectors, which
we define below, and which define valid kernels.
The two commonly used families of kernels are polynomial kernels and
radial basis functions. Polynomial kernels are of the form K(x, z) = (1 +
Online edition (c) 2009 Cambridge UP
15.2 Extensions to the SVM model 333
xT
z)d. The case of d = 1 is a linear kernel, which is what we had before the
start of this section (the constant 1 just changing the threshold). The case of
d = 2 gives a quadratic kernel, and is very commonly used. We illustrated
the quadratic kernel in Example 15.2.
The most common form of radial basis function is a Gaussian distribution,
calculated as:
K(x, z) = e−(x−z)2/(2σ2)
(15.16)
A radial basis function (rbf) is equivalent to mapping the data into an infinite
dimensional Hilbert space, and so we cannot illustrate the radial basis
function concretely, as we did a quadratic kernel. Beyond these two families,
there has been interesting work developing other kernels, some of which is
promising for text applications. In particular, there has been investigation of
string kernels (see Section 15.5).
The world of SVMs comes with its own language, which is rather different
from the language otherwise used in machine learning. The terminology
does have deep roots in mathematics, but it’s important not to be too awed
by that terminology. Really, we are talking about some quite simple things. A
polynomial kernel allows us to model feature conjunctions (up to the order of
the polynomial). That is, if we want to be able to model occurrences of pairs
of words, which give distinctive information about topic classification, not
given by the individual words alone, like perhaps operating AND system or
ethnic AND cleansing, then we need to use a quadratic kernel. If occurrences
of triples of words give distinctive information, then we need to use a cubic
kernel. Simultaneously you also get the powers of the basic features – for
most text applications, that probably isn’t useful, but just comes along with
the math and hopefully doesn’t do harm. A radial basis function allows you
to have features that pick out circles (hyperspheres) – although the decision
boundaries become much more complex as multiple such features interact. A
string kernel lets you have features that are character subsequences of terms.
All of these are straightforward notions which have also been used in many
other places under different names.
15.2.4 Experimental results
We presented results in Section 13.6 showing that an SVM is a very effective
text classifier. The results of Dumais et al. (1998) given in Table 13.9
show SVMs clearly performing the best. This was one of several pieces of
work from this time that established the strong reputation of SVMs for text
classification. Another pioneering work on scaling and evaluating SVMs
for text classification was (Joachims 1998). We present some of his results
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334 15 Support vector machines and machine learning on documents
Roc- Dec. linear SVM rbf-SVM
NB chio Trees kNN C = 0.5 C = 1.0 σ ≈ 7
earn 96.0 96.1 96.1 97.8 98.0 98.2 98.1
acq 90.7 92.1 85.3 91.8 95.5 95.6 94.7
money-fx 59.6 67.6 69.4 75.4 78.8 78.5 74.3
grain 69.8 79.5 89.1 82.6 91.9 93.1 93.4
crude 81.2 81.5 75.5 85.8 89.4 89.4 88.7
trade 52.2 77.4 59.2 77.9 79.2 79.2 76.6
interest 57.6 72.5 49.1 76.7 75.6 74.8 69.1
ship 80.9 83.1 80.9 79.8 87.4 86.5 85.8
wheat 63.4 79.4 85.5 72.9 86.6 86.8 82.4
corn 45.2 62.2 87.7 71.4 87.5 87.8 84.6
microavg. 72.3 79.9 79.4 82.6 86.7 87.5 86.4
◮ Table 15.2 SVM classifier break-even F1 from (Joachims 2002a, p. 114). Results
are shown for the 10 largest categories and for microaveraged performance over all
90 categories on the Reuters-21578 data set.
from (Joachims 2002a) in Table 15.2.5 Joachims used a large number of term
features in contrast to Dumais et al. (1998), who used MI feature selection
(Section 13.5.1, page 272) to build classifiers with a much more limited number
of features. The success of the linear SVM mirrors the results discussed
in Section 14.6 (page 308) on other linear approaches like Naive Bayes. It
seems that working with simple term features can get one a long way. It is
again noticeable the extent to which different papers’ results for the same machine
learning methods differ. In particular, based on replications by other
researchers, the Naive Bayes results of (Joachims 1998) appear too weak, and
the results in Table 13.9 should be taken as representative.
15.3 Issues in the classification of text documents
There are lots of applications of text classification in the commercial world;
email spam filtering is perhaps now the most ubiquitous. Jackson and Moulinier
(2002) write: “There is no question concerning the commercial value of
being able to classify documents automatically by content. There are myriad
5. These results are in terms of the break-even F1 (see Section 8.4). Many researchers disprefer
this measure for text classification evaluation, since its calculation may involve interpolation
rather than an actual parameter setting of the system and it is not clear why this value should
be reported rather than maximal F1 or another point on the precision/recall curve motivated by
the task at hand. While earlier results in (Joachims 1998) suggested notable gains on this task
from the use of higher order polynomial or rbf kernels, this was with hard-margin SVMs. With
soft-margin SVMs, a simple linear SVM with the default C = 1 performs best.
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15.3 Issues in the classification of text documents 335
potential applications of such a capability for corporate Intranets, government
departments, and Internet publishers.”
Most of our discussion of classification has focused on introducing various
machine learning methods rather than discussing particular features of text
documents relevant to classification. This bias is appropriate for a textbook,
but is misplaced for an application developer. It is frequently the case that
greater performance gains can be achieved from exploiting domain-specific
text features than from changing from one machine learning method to another.
Jackson and Moulinier (2002) suggest that “Understanding the data
is one of the keys to successful categorization, yet this is an area in which
most categorization tool vendors are extremely weak. Many of the ‘one size
fits all’ tools on the market have not been tested on a wide range of content
types.” In this section we wish to step back a little and consider the applications
of text classification, the space of possible solutions, and the utility of
application-specific heuristics.
15.3.1 Choosing what kind of classifier to use
When confronted with a need to build a text classifier, the first question to
ask is how much training data is there currently available? None? Very little?
Quite a lot? Or a huge amount, growing every day? Often one of the biggest
practical challenges in fielding a machine learning classifier in real applications
is creating or obtaining enough training data. For many problems and
algorithms, hundreds or thousands of examples from each class are required
to produce a high performance classifier and many real world contexts involve
large sets of categories. We will initially assume that the classifier is
needed as soon as possible; if a lot of time is available for implementation,
much of it might be spent on assembling data resources.
If you have no labeled training data, and especially if there are existing
staff knowledgeable about the domain of the data, then you should never
forget the solution of using hand-written rules. That is, you write standing
queries, as we touched on at the beginning of Chapter 13. For example:
IF (wheat OR grain) AND NOT (whole OR bread) THEN c = grain
In practice, rules get a lot bigger than this, and can be phrased using more
sophisticated query languages than just Boolean expressions, including the
use of numeric scores. With careful crafting (that is, by humans tuning the
rules on development data), the accuracy of such rules can become very high.
Jacobs and Rau (1990) report identifying articles about takeovers with 92%
precision and 88.5% recall, and Hayes and Weinstein (1990) report 94% recall
and 84% precision over 675 categories on Reuters newswire documents.
Nevertheless the amount of work to create such well-tuned rules is very
large. A reasonable estimate is 2 days per class, and extra time has to go
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336 15 Support vector machines and machine learning on documents
into maintenance of rules, as the content of documents in classes drifts over
time (cf. page 269).
If you have fairly little data and you are going to train a supervised classifier,
then machine learning theory says you should stick to a classifier with
high bias, as we discussed in Section 14.6 (page 308). For example, there
are theoretical and empirical results that Naive Bayes does well in such circumstances
(Ng and Jordan 2001, Forman and Cohen 2004), although this
effect is not necessarily observed in practice with regularized models over
textual data (Klein and Manning 2002). At any rate, a very low bias model
like a nearest neighbor model is probably counterindicated. Regardless, the
quality of the model will be adversely affected by the limited training data.
Here, the theoretically interesting answer is to try to apply semi-supervisedSEMI-SUPERVISED
LEARNING training methods. This includes methods such as bootstrapping or the EM
algorithm, which we will introduce in Section 16.5 (page 368). In these methods,
the system gets some labeled documents, and a further large supply
of unlabeled documents over which it can attempt to learn. One of the big
advantages of Naive Bayes is that it can be straightforwardly extended to
be a semi-supervised learning algorithm, but for SVMs, there is also semisupervised
learning work which goes under the title of transductive SVMs.TRANSDUCTIVE SVMS
See the references for pointers.
Often, the practical answer is to work out how to get more labeled data as
quickly as you can. The best way to do this is to insert yourself into a process
where humans will be willing to label data for you as part of their natural
tasks. For example, in many cases humans will sort or route email for their
own purposes, and these actions give information about classes. The alternative
of getting human labelers expressly for the task of training classifiers
is often difficult to organize, and the labeling is often of lower quality, because
the labels are not embedded in a realistic task context. Rather than
getting people to label all or a random sample of documents, there has also
been considerable research on active learning, where a system is built whichACTIVE LEARNING
decides which documents a human should label. Usually these are the ones
on which a classifier is uncertain of the correct classification. This can be effective
in reducing annotation costs by a factor of 2–4, but has the problem
that the good documents to label to train one type of classifier often are not
the good documents to label to train a different type of classifier.
If there is a reasonable amount of labeled data, then you are in the perfect
position to use everything that we have presented about text classification.
For instance, you may wish to use an SVM. However, if you are
deploying a linear classifier such as an SVM, you should probably design
an application that overlays a Boolean rule-based classifier over the machine
learning classifier. Users frequently like to adjust things that do not come
out quite right, and if management gets on the phone and wants the classification
of a particular document fixed right now, then this is much easier to
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15.3 Issues in the classification of text documents 337
do by hand-writing a rule than by working out how to adjust the weights
of an SVM without destroying the overall classification accuracy. This is one
reason why machine learning models like decision trees which produce userinterpretable
Boolean-like models retain considerable popularity.
If a huge amount of data are available, then the choice of classifier probably
has little effect on your results and the best choice may be unclear (cf. Banko
and Brill 2001). It may be best to choose a classifier based on the scalability
of training or even runtime efficiency. To get to this point, you need to have
huge amounts of data. The general rule of thumb is that each doubling of
the training data size produces a linear increase in classifier performance,
but with very large amounts of data, the improvement becomes sub-linear.
15.3.2 Improving classifier performance
For any particular application, there is usually significant room for improving
classifier effectiveness through exploiting features specific to the domain
or document collection. Often documents will contain zones which are especially
useful for classification. Often there will be particular subvocabularies
which demand special treatment for optimal classification effectiveness.
Large and difficult category taxonomies
If a text classification problem consists of a small number of well-separated
categories, then many classification algorithms are likely to work well. But
many real classification problems consist of a very large number of often
very similar categories. The reader might think of examples like web directories
(the Yahoo! Directory or the Open Directory Project), library classification
schemes (Dewey Decimal or Library of Congress) or the classification
schemes used in legal or medical applications. For instance, the Yahoo!
Directory consists of over 200,000 categories in a deep hierarchy. Accurate
classification over large sets of closely related classes is inherently difficult.
Most large sets of categories have a hierarchical structure, and attempting
to exploit the hierarchy by doing hierarchical classification is a promising ap-HIERARCHICAL
CLASSIFICATION proach. However, at present the effectiveness gains from doing this rather
than just working with the classes that are the leaves of the hierarchy remain
modest.6 But the technique can be very useful simply to improve the
scalability of building classifiers over large hierarchies. Another simple way
to improve the scalability of classifiers over large hierarchies is the use of
aggressive feature selection. We provide references to some work on hierarchical
classification in Section 15.5.
6. Using the small hierarchy in Figure 13.1 (page 257) as an example, the leaf classes are ones
like poultry and coffee, as opposed to higher-up classes like industries.
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338 15 Support vector machines and machine learning on documents
A general result in machine learning is that you can always get a small
boost in classification accuracy by combining multiple classifiers, provided
only that the mistakes that they make are at least somewhat independent.
There is now a large literature on techniques such as voting, bagging, and
boosting multiple classifiers. Again, there are some pointers in the references.
Nevertheless, ultimately a hybrid automatic/manual solution may be
needed to achieve sufficient classification accuracy. A common approach in
such situations is to run a classifier first, and to accept all its high confidence
decisions, but to put low confidence decisions in a queue for manual review.
Such a process also automatically leads to the production of new training
data which can be used in future versions of the machine learning classifier.
However, note that this is a case in point where the resulting training data is
clearly not randomly sampled from the space of documents.
Features for text
The default in both ad hoc retrieval and text classification is to use terms
as features. However, for text classification, a great deal of mileage can be
achieved by designing additional features which are suited to a specific problem.
Unlike the case of IR query languages, since these features are internal
to the classifier, there is no problem of communicating these features to an
end user. This process is generally referred to as feature engineering. At pre-FEATURE ENGINEERING
sent, feature engineering remains a human craft, rather than something done
by machine learning. Good feature engineering can often markedly improve
the performance of a text classifier. It is especially beneficial in some of the
most important applications of text classification, like spam and porn filter-
ing.
Classification problems will often contain large numbers of terms which
can be conveniently grouped, and which have a similar vote in text classification
problems. Typical examples might be year mentions or strings of
exclamation marks. Or they may be more specialized tokens like ISBNs or
chemical formulas. Often, using them directly in a classifier would greatly increase
the vocabulary without providing classificatory power beyond knowing
that, say, a chemical formula is present. In such cases, the number of
features and feature sparseness can be reduced by matching such items with
regular expressions and converting them into distinguished tokens. Consequently,
effectiveness and classifier speed are normally enhanced. Sometimes
all numbers are converted into a single feature, but often some value
can be had by distinguishing different kinds of numbers, such as four digit
numbers (which are usually years) versus other cardinal numbers versus real
numbers with a decimal point. Similar techniques can be applied to dates,
ISBN numbers, sports game scores, and so on.
Going in the other direction, it is often useful to increase the number of fea-
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15.3 Issues in the classification of text documents 339
tures by matching parts of words, and by matching selected multiword patterns
that are particularly discriminative. Parts of words are often matched
by character k-gram features. Such features can be particularly good at providing
classification clues for otherwise unknown words when the classifier
is deployed. For instance, an unknown word ending in -rase is likely to be an
enzyme, even if it wasn’t seen in the training data. Good multiword patterns
are often found by looking for distinctively common word pairs (perhaps
using a mutual information criterion between words, in a similar way to
its use in Section 13.5.1 (page 272) for feature selection) and then using feature
selection methods evaluated against classes. They are useful when the
components of a compound would themselves be misleading as classification
cues. For instance, this would be the case if the keyword ethnic was
most indicative of the categories food and arts, the keyword cleansing was
most indicative of the category home, but the collocation ethnic cleansing instead
indicates the category world news. Some text classifiers also make use
of features from named entity recognizers (cf. page 195).
Do techniques like stemming and lowercasing (Section 2.2, page 22) help
for text classification? As always, the ultimate test is empirical evaluations
conducted on an appropriate test collection. But it is nevertheless useful to
note that such techniques have a more restricted chance of being useful for
classification. For IR, you often need to collapse forms of a word like oxygenate
and oxygenation, because the appearance of either in a document is a
good clue that the document will be relevant to a query about oxygenation.
Given copious training data, stemming necessarily delivers no value for text
classification. If several forms that stem together have a similar signal, the
parameters estimated for all of them will have similar weights. Techniques
like stemming help only in compensating for data sparseness. This can be
a useful role (as noted at the start of this section), but often different forms
of a word can convey significantly different cues about the correct document
classification. Overly aggressive stemming can easily degrade classification
performance.
Document zones in text classification
As already discussed in Section 6.1, documents usually have zones, such as
mail message headers like the subject and author, or the title and keywords
of a research article. Text classifiers can usually gain from making use of
these zones during training and classification.
Upweighting document zones. In text classification problems, you can frequently
get a nice boost to effectiveness by differentially weighting contributions
from different document zones. Often, upweighting title words is
particularly effective (Cohen and Singer 1999, p. 163). As a rule of thumb,
Online edition (c) 2009 Cambridge UP
340 15 Support vector machines and machine learning on documents
it is often effective to double the weight of title words in text classification
problems. You can also get value from upweighting words from pieces of
text that are not so much clearly defined zones, but where nevertheless evidence
from document structure or content suggests that they are important.
Murata et al. (2000) suggest that you can also get value (in an ad hoc retrieval
context) from upweighting the first sentence of a (newswire) document.
Separate feature spaces for document zones. There are two strategies that
can be used for document zones. Above we upweighted words that appear
in certain zones. This means that we are using the same features (that is, parameters
are “tied” across different zones), but we pay more attention to thePARAMETER TYING
occurrence of terms in particular zones. An alternative strategy is to have a
completely separate set of features and corresponding parameters for words
occurring in different zones. This is in principle more powerful: a word
could usually indicate the topic Middle East when in the title but Commodities
when in the body of a document. But, in practice, tying parameters is usually
more successful. Having separate feature sets means having two or more
times as many parameters, many of which will be much more sparsely seen
in the training data, and hence with worse estimates, whereas upweighting
has no bad effects of this sort. Moreover, it is quite uncommon for words to
have different preferences when appearing in different zones; it is mainly the
strength of their vote that should be adjusted. Nevertheless, ultimately this
is a contingent result, depending on the nature and quantity of the training
data.
Connections to text summarization. In Section 8.7, we mentioned the field
of text summarization, and how most work in that field has adopted the
limited goal of extracting and assembling pieces of the original text that are
judged to be central based on features of sentences that consider the sentence’s
position and content. Much of this work can be used to suggest zones
that may be distinctively useful for text classification. For example Kołcz
et al. (2000) consider a form of feature selection where you classify documents
based only on words in certain zones. Based on text summarization
research, they consider using (i) only the title, (ii) only the first paragraph,
(iii) only the paragraph with the most title words or keywords, (iv) the first
two paragraphs or the first and last paragraph, or (v) all sentences with a
minimum number of title words or keywords. In general, these positional
feature selection methods produced as good results as mutual information
(Section 13.5.1), and resulted in quite competitive classifiers. Ko et al. (2004)
also took inspiration from text summarization research to upweight sentences
with either words from the title or words that are central to the document’s
content, leading to classification accuracy gains of almost 1%. This
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15.4 Machine learning methods in ad hoc information retrieval 341
presumably works because most such sentences are somehow more central
to the concerns of the document.
?
Exercise 15.4 [⋆⋆]
Spam email often makes use of various cloaking techniques to try to get through. One
method is to pad or substitute characters so as to defeat word-based text classifiers.
For example, you see terms like the following in spam email:
Rep1icaRolex bonmus Viiiaaaagra pi11z
PHARlbdMACY [LEV]i[IT]l[RA] se∧xual ClAfLlS
Discuss how you could engineer features that would largely defeat this strategy.
Exercise 15.5 [⋆⋆]
Another strategy often used by purveyors of email spam is to follow the message
they wish to send (such as buying a cheap stock or whatever) with a paragraph of
text from another innocuous source (such as a news article). Why might this strategy
be effective? How might it be addressed by a text classifier?
Exercise 15.6 [⋆]
What other kinds of features appear as if they would be useful in an email spam
classifier?
15.4 Machine learning methods in ad hoc information retrieval
Rather than coming up with term and document weighting functions by
hand, as we primarily did in Chapter 6, we can view different sources of relevance
signal (cosine score, title match, etc.) as features in a learning problem.
A classifier that has been fed examples of relevant and nonrelevant documents
for each of a set of queries can then figure out the relative weights
of these signals. If we configure the problem so that there are pairs of a
document and a query which are assigned a relevance judgment of relevant
or nonrelevant, then we can think of this problem too as a text classification
problem. Taking such a classification approach is not necessarily best, and
we present an alternative in Section 15.4.2. Nevertheless, given the material
we have covered, the simplest place to start is to approach this problem as
a classification problem, by ordering the documents according to the confidence
of a two-class classifier in its relevance decision. And this move is not
purely pedagogical; exactly this approach is sometimes used in practice.
15.4.1 A simple example of machine-learned scoring
In this section we generalize the methodology of Section 6.1.2 (page 113) to
machine learning of the scoring function. In Section 6.1.2 we considered a
case where we had to combine Boolean indicators of relevance; here we consider
more general factors to further develop the notion of machine-learned
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342 15 Support vector machines and machine learning on documents
Example DocID Query Cosine score ω Judgment
Φ1 37 linux operating system 0.032 3 relevant
Φ2 37 penguin logo 0.02 4 nonrelevant
Φ3 238 operating system 0.043 2 relevant
Φ4 238 runtime environment 0.004 2 nonrelevant
Φ5 1741 kernel layer 0.022 3 relevant
Φ6 2094 device driver 0.03 2 relevant
Φ7 3191 device driver 0.027 5 nonrelevant
· · · · · · · · · · · · · · · · · ·
◮ Table 15.3 Training examples for machine-learned scoring.
relevance. In particular, the factors we now consider go beyond Boolean
functions of query term presence in document zones, as in Section 6.1.2.
We develop the ideas in a setting where the scoring function is a linear
combination of two factors: (1) the vector space cosine similarity between
query and document and (2) the minimum window width ω within which
the query terms lie. As we noted in Section 7.2.2 (page 144), query term
proximity is often very indicative of a document being on topic, especially
with longer documents and on the web. Among other things, this quantity
gives us an implementation of implicit phrases. Thus we have one factor that
depends on the statistics of query terms in the document as a bag of words,
and another that depends on proximity weighting. We consider only two
features in the development of the ideas because a two-feature exposition
remains simple enough to visualize. The technique can be generalized to
many more features.
As in Section 6.1.2, we are provided with a set of training examples, each
of which is a pair consisting of a query and a document, together with a
relevance judgment for that document on that query that is either relevant or
nonrelevant. For each such example we can compute the vector space cosine
similarity, as well as the window width ω. The result is a training set as
shown in Table 15.3, which resembles Figure 6.5 (page 115) from Section 6.1.2.
Here, the two features (cosine score denoted α and window width ω) are
real-valued predictors. If we once again quantify the judgment relevant as 1
and nonrelevant as 0, we seek a scoring function that combines the values of
the features to generate a value that is (close to) 0 or 1. We wish this function
to be in agreement with our set of training examples as far as possible.
Without loss of generality, a linear classifier will use a linear combination of
features of the form
Score(d, q) = Score(α, ω) = aα + bω + c,(15.17)
with the coefficients a, b, c to be learned from the training data. While it is
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15.4 Machine learning methods in ad hoc information retrieval 343
0
2 3 4 5
0 . 0 5
0 . 0 2 5
c
o
s
i n
e
s
c
o
r
e

T e r m p r o x i m i t y $
R
R
R
R
R
R
R
R
R
R
R
N
N
N
N
N
N
N
N
N
N
◮ Figure 15.7 A collection of training examples. Each R denotes a training example
labeled relevant, while each N is a training example labeled nonrelevant.
possible to formulate this as an error minimization problem as we did in
Section 6.1.2, it is instructive to visualize the geometry of Equation (15.17).
The examples in Table 15.3 can be plotted on a two-dimensional plane with
axes corresponding to the cosine score α and the window width ω. This is
depicted in Figure 15.7.
In this setting, the function Score(α, ω) from Equation (15.17) represents
a plane “hanging above” Figure 15.7. Ideally this plane (in the direction
perpendicular to the page containing Figure 15.7) assumes values close to
1 above the points marked R, and values close to 0 above the points marked
N. Since a plane is unlikely to assume only values close to 0 or 1 above the
training sample points, we make use of thresholding: given any query and
document for which we wish to determine relevance, we pick a value θ and
if Score(α, ω) > θ we declare the document to be relevant, else we declare
the document to be nonrelevant. As we know from Figure 14.8 (page 301),
all points that satisfy Score(α, ω) = θ form a line (shown as a dashed line
in Figure 15.7) and we thus have a linear classifier that separates relevant
Online edition (c) 2009 Cambridge UP
344 15 Support vector machines and machine learning on documents
from nonrelevant instances. Geometrically, we can find the separating line
as follows. Consider the line passing through the plane Score(α, ω) whose
height is θ above the page containing Figure 15.7. Project this line down onto
Figure 15.7; this will be the dashed line in Figure 15.7. Then, any subsequent
query/document pair that falls below the dashed line in Figure 15.7 is
deemed nonrelevant; above the dashed line, relevant.
Thus, the problem of making a binary relevant/nonrelevant judgment given
training examples as above turns into one of learning the dashed line in Figure
15.7 separating relevant training examples from the nonrelevant ones. Being
in the α-ω plane, this line can be written as a linear equation involving
α and ω, with two parameters (slope and intercept). The methods of linear
classification that we have already looked at in Chapters 13–15 provide
methods for choosing this line. Provided we can build a sufficiently rich collection
of training samples, we can thus altogether avoid hand-tuning score
functions as in Section 7.2.3 (page 145). The bottleneck of course is the ability
to maintain a suitably representative set of training examples, whose relevance
assessments must be made by experts.
15.4.2 Result ranking by machine learning
The above ideas can be readily generalized to functions of many more than
two variables. There are lots of other scores that are indicative of the relevance
of a document to a query, including static quality (PageRank-style
measures, discussed in Chapter 21), document age, zone contributions, document
length, and so on. Providing that these measures can be calculated
for a training document collection with relevance judgments, any number
of such measures can be used to train a machine learning classifier. For instance,
we could train an SVM over binary relevance judgments, and order
documents based on their probability of relevance, which is monotonic with
the documents’ signed distance from the decision boundary.
However, approaching IR result ranking like this is not necessarily the
right way to think about the problem. Statisticians normally first divide
problems into classification problems (where a categorical variable is predicted)
versus regression problems (where a real number is predicted). InREGRESSION
between is the specialized field of ordinal regression where a ranking is pre-ORDINAL REGRESSION
dicted. Machine learning for ad hoc retrieval is most properly thought of as
an ordinal regression problem, where the goal is to rank a set of documents
for a query, given training data of the same sort. This formulation gives
some additional power, since documents can be evaluated relative to other
candidate documents for the same query, rather than having to be mapped
to a global scale of goodness, while also weakening the problem space, since
just a ranking is required rather than an absolute measure of relevance. Issues
of ranking are especially germane in web search, where the ranking at
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15.4 Machine learning methods in ad hoc information retrieval 345
the very top of the results list is exceedingly important, whereas decisions
of relevance of a document to a query may be much less important. Such
work can and has been pursued using the structural SVM framework which
we mentioned in Section 15.2.2, where the class being predicted is a ranking
of results for a query, but here we will present the slightly simpler ranking
SVM.
The construction of a ranking SVM proceeds as follows. We begin with aRANKING SVM
set of judged queries. For each training query q, we have a set of documents
returned in response to the query, which have been totally ordered by a person
for relevance to the query. We construct a vector of features ψj = ψ(dj, q)
for each document/query pair, using features such as those discussed in Section
15.4.1, and many more. For two documents di and dj, we then form the
vector of feature differences:
Φ(di, dj, q) = ψ(di, q) − ψ(dj, q)(15.18)
By hypothesis, one of di and dj has been judged more relevant. If di is
judged more relevant than dj, denoted di ≺ dj (di should precede dj in the
results ordering), then we will assign the vector Φ(di, dj, q) the class yijq =
+1; otherwise −1. The goal then is to build a classifier which will return
wT
Φ(di, dj, q) > 0 iff di ≺ dj(15.19)
This SVM learning task is formalized in a manner much like the other examples
that we saw before:
(15.20) Find w, and ξi,j ≥ 0 such that:
• 1
2 wT
w + C ∑i,j ξi,j is minimized
• and for all {Φ(di, dj, q) : di ≺ dj}, wT
Φ(di, dj, q) ≥ 1 − ξi,j
We can leave out yijq in the statement of the constraint, since we only need
to consider the constraint for document pairs ordered in one direction, since
≺ is antisymmetric. These constraints are then solved, as before, to give
a linear classifier which can rank pairs of documents. This approach has
been used to build ranking functions which outperform standard hand-built
ranking functions in IR evaluations on standard data sets; see the references
for papers that present such results.
Both of the methods that we have just looked at use a linear weighting
of document features that are indicators of relevance, as has most work in
this area. It is therefore perhaps interesting to note that much of traditional
IR weighting involves nonlinear scaling of basic measurements (such as logweighting
of term frequency, or idf). At the present time, machine learning is
very good at producing optimal weights for features in a linear combination
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346 15 Support vector machines and machine learning on documents
(or other similar restricted model classes), but it is not good at coming up
with good nonlinear scalings of basic measurements. This area remains the
domain of human feature engineering.
The idea of learning ranking functions has been around for a number of
years, but it is only very recently that sufficient machine learning knowledge,
training document collections, and computational power have come together
to make this method practical and exciting. It is thus too early to write something
definitive on machine learning approaches to ranking in information
retrieval, but there is every reason to expect the use and importance of machine
learned ranking approaches to grow over time. While skilled humans
can do a very good job at defining ranking functions by hand, hand tuning
is difficult, and it has to be done again for each new document collection and
class of users.
?
Exercise 15.7
Plot the first 7 rows of Table 15.3 in the α-ω plane to produce a figure like that in
Figure 15.7.
Exercise 15.8
Write down the equation of a line in the α-ω plane separating the Rs from the Ns.
Exercise 15.9
Give a training example (consisting of values for α, ω and the relevance judgment)
that when added to the training set makes it impossible to separate the R’s from the
N’s using a line in the α-ω plane.
15.5 References and further reading
The somewhat quirky name support vector machine originates in the neural
networks literature, where learning algorithms were thought of as architectures,
and often referred to as “machines”. The distinctive element of
this model is that the decision boundary to use is completely decided (“supported”)
by a few training data points, the support vectors.
For a more detailed presentation of SVMs, a good, well-known articlelength
introduction is (Burges 1998). Chen et al. (2005) introduce the more
recent ν-SVM, which provides an alternative parameterization for dealing
with inseparable problems, whereby rather than specifying a penalty C, you
specify a parameter ν which bounds the number of examples which can appear
on the wrong side of the decision surface. There are now also several
books dedicated to SVMs, large margin learning, and kernels: (Cristianini
and Shawe-Taylor 2000) and (Schölkopf and Smola 2001) are more mathematically
oriented, while (Shawe-Taylor and Cristianini 2004) aims to be
more practical. For the foundations by their originator, see (Vapnik 1998).
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15.5 References and further reading 347
Some recent, more general books on statistical learning, such as (Hastie et al.
2001) also give thorough coverage of SVMs.
The construction of multiclass SVMs is discussed in (Weston and Watkins
1999), (Crammer and Singer 2001), and (Tsochantaridis et al. 2005). The last
reference provides an introduction to the general framework of structural
SVMs.
The kernel trick was first presented in (Aizerman et al. 1964). For more
about string kernels and other kernels for structured data, see (Lodhi et al.
2002) and (Gaertner et al. 2002). The Advances in Neural Information Processing
(NIPS) conferences have become the premier venue for theoretical
machine learning work, such as on SVMs. Other venues such as SIGIR are
much stronger on experimental methodology and using text-specific features
to improve classifier effectiveness.
A recent comparison of most current machine learning classifiers (though
on problems rather different from typical text problems) can be found in
(Caruana and Niculescu-Mizil 2006). (Li and Yang 2003), discussed in Section
13.6, is the most recent comparative evaluation of machine learning classifiers
on text classification. Older examinations of classifiers on text problems
can be found in (Yang 1999, Yang and Liu 1999, Dumais et al. 1998).
Joachims (2002a) presents his work on SVMs applied to text problems in detail.
Zhang and Oles (2001) present an insightful comparison of Naive Bayes,
regularized logistic regression and SVM classifiers.
Joachims (1999) discusses methods of making SVM learning practical over
large text data sets. Joachims (2006a) improves on this work.
A number of approaches to hierarchical classification have been developed
in order to deal with the common situation where the classes to be assigned
have a natural hierarchical organization (Koller and Sahami 1997, McCallum
et al. 1998, Weigend et al. 1999, Dumais and Chen 2000). In a recent
large study on scaling SVMs to the entire Yahoo! directory, Liu et al. (2005)
conclude that hierarchical classification noticeably if still modestly outperforms
flat classification. Classifier effectiveness remains limited by the very
small number of training documents for many classes. For a more general
approach that can be applied to modeling relations between classes, which
may be arbitrary rather than simply the case of a hierarchy, see Tsochantaridis
et al. (2005).
Moschitti and Basili (2004) investigate the use of complex nominals, proper
nouns and word senses as features in text classification.
Dietterich (2002) overviews ensemble methods for classifier combination,
while Schapire (2003) focuses particularly on boosting, which is applied to
text classification in (Schapire and Singer 2000).
Chapelle et al. (2006) present an introduction to work in semi-supervised
methods, including in particular chapters on using EM for semi-supervised
text classification (Nigam et al. 2006) and on transductive SVMs (Joachims
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348 15 Support vector machines and machine learning on documents
2006b). Sindhwani and Keerthi (2006) present a more efficient implementation
of a transductive SVM for large data sets.
Tong and Koller (2001) explore active learning with SVMs for text classification;
Baldridge and Osborne (2004) point out that examples selected for
annotation with one classifier in an active learning context may be no better
than random examples when used with another classifier.
Machine learning approaches to ranking for ad hoc retrieval were pioneered
in (Wong et al. 1988), (Fuhr 1992), and (Gey 1994). But limited training
data and poor machine learning techniques meant that these pieces of work
achieved only middling results, and hence they only had limited impact at
the time.
Taylor et al. (2006) study using machine learning to tune the parameters
of the BM25 family of ranking functions (Section 11.4.3, page 232) so as to
maximize NDCG (Section 8.4, page 163). Machine learning approaches to
ordinal regression appear in (Herbrich et al. 2000) and (Burges et al. 2005),
and are applied to clickstream data in (Joachims 2002b). Cao et al. (2006)
study how to make this approach effective in IR, and Qin et al. (2007) suggest
an extension involving using multiple hyperplanes. Yue et al. (2007) study
how to do ranking with a structural SVM approach, and in particular show
how this construction can be effectively used to directly optimize for MAP
(Section 8.4, page 158), rather than using surrogate measures like accuracy or
area under the ROC curve. Geng et al. (2007) study feature selection for the
ranking problem.
Other approaches to learning to rank have also been shown to be effective
for web search, such as (Burges et al. 2005, Richardson et al. 2006).