FUZZY INTERVAL NUMBER (FIN) TECHNIQUES
FOR MULTILINGUAL AND CROSS LANGUAGE
INFORMATION RETRIEVAL
Theodoros Alevizos, Vassilis G. Kaburlasos, Stelios Papadakis
Department of Industrial Informatics, T.E.I. of Kavala, Greece
Christos Skourlas, Petros Belsis
Department of Informatics, T.E.I. of Athens, Greece
Keywords: Fuzzy Interval Number (FIN), Information Retrieval (IR), Cross Language Information Retrieval (CLIR).
Abstract: Fuzzy Interval Numbers (FINs) could be seen as a set of techniques applied in Fuzzy System applications.
In this paper, we propose a series of techniques to solve multi-Lingual and Cross Language Information
Retrieval (CLIR) problems, based on Fuzzy Interval Numbers (FINs). Some experiments showing the
importance of these techniques in the CLIR-systems are briefly described and discussed. Our method is
evaluated using monolingual and bilingual public bibliographic data extracted from the National Archive of
the Greek National Documentation Centre. All the experiments were conducted with and without the use of
stemming, stop-words and other language dependent (pre-) processing techniques. It seems that a main
advantage of our approach is that the method is language independent and there is also no need for any text
pre-processing or higher level processing, avoiding thus the use of taggers, parsers, feature selection
strategies, or the use of other language dependent NLP tools.
1 INTRODUCTION
Fuzzy (set) techniques were proposed for
Information Retrieval (IR) applications many years
ago (Radecki, 1979), (Kraft, 1993), mainly for
modelling. Fuzzy Interval Numbers (FINs) and the
related theoretical (mathematical) background,
which is based on the metric space of the
generalized intervals, were introduced by
Kaburlasos (Kaburlasos, 2004), (Petridis et al, 2003)
in fuzzy system applications. A FIN (see Figure 1)
may be interpreted as a conventional fuzzy set;
additional interpretations for a FIN are possible
including a statistical interpretation.
Fuzzy Interval Numbers (FINs) and lattice
algorithms have been employed in various real-
world applications including numeric and non-
numeric data. Kaburlasos and Petridis (Kaburlasos et
al, 2000), Petridis and Kaburlasos (Petridis et al,
1998, 2000, 2001) presented FLN (Fuzzy Lattice
Neurocomputing mainly for competitive clustering.
The most popular among the FLN models for
clustering is σ-FLN. For example, (Petridis and
Kaburlazos, 2000) describe the automated –
electromechanical surgical mechatronics tool which
is used to control penetration through soft tissues in
the epidural punctury. In this framework a series of
learning experiments for soft tissues recognition was
carried out and the σ-FLN model and the Voting σ-
FLNMAP algorithm were applied.
FLN algorithms were also used in stapedotomy
surgery (Kaburlasos et al, 1997), and prediction of
ozone concentration by classification, based on
meteorological and air quality data (Athanasiadis
and Mitkas, 2004).
(Petridis et al, 2001), (Kaburlasos et al, 2002),
(Petridis and Kaburlasos, 2003) reported a best sugar
prediction accuracy using Fuzzy Interval Numbers
and the FINkNN classifier and a population
(measurements) of production and meteorological
data. (Kaburlasos et al, 2005) used FINs for
representing geometric and other fertilizer granule
features. This type of modelling was used to cover
needs of the Greek Fertilizer Industry.
(Marinagi et al, 2006) proposed the use of FINs
classifier to handle problems of Cross Language
348
Alevizos T., G. Kaburlasos V., Papadakis S., Skourlas C. and Belsis P. (2007).
FUZZY INTERVAL NUMBER (FIN) TECHNIQUES FOR MULTILINGUAL AND CROSS LANGUAGE INFORMATION RETRIEVAL.
In Proceedings of the Ninth International Conference on Enterprise Information Systems - AIDSS, pages 348-355
DOI: 10.5220/0002401503480355
Copyright
c
 SciTePress

Information Retrieval. The basic features of this
method are the following:
1) Documents are represented as FINs (see
Figure 1).
2) The FIN representation of documents is based
on the use of the collection term frequency as the
term identifier.
3) The use of FIN distance instead of a similarity
measure.
For the distance calculations a bell-shaped mass
function was used:
m
h
(t) =
2
2
2
)max(
⎟
⎠
⎞
⎜
⎝
⎛
−+
+
ctf
tA
h
β
α
The positive real numbers A, α, β, are
parameters; max(ctf) is the maximum value of all
the collection term frequencies.
The structure of the remainder of this paper is as
follows:
In section 2 a brief introduction to the
mathematical background of the generalized
intervals and other relevant concepts is given. The
notation used (Kaburlasos, 2004) is an evolution
(simplification) of the one described by (Marinagi et
al, 2006). In section 3 the “conceptual” transition
from document vectors to document FINs is
presented. Section 4 presents how to calculate the
similarity of documents using Fuzzy Interval
Numbers. In section 5 we present our experiments
and evaluate and discuss our method. Conclusions
and current work on the topic are presented in
section 6.
2 THEORETICAL
BACKGROUND
2.1 Generalised Intervals
A positive generalized interval of height h ∈ (0,1] is
a map
12
[,]
:{0,}
h
xx
R
h μ ⎯⎯→ , given by:
where x
1
≤ x
2
A negative generalized interval of height h(∈
(0,1] is a map
12
[,]
:{0,}
h
xx
R
h μ
⎯
⎯→−, given by:
where x
1
> x
2
We shall use below the more compact notation
[x
1
, x
2
]
h
instead of the μ notation.
The interpretation of a generalized interval
depends on an application; for instance if a feature is
present it could be indicated by a positive
generalized interval. Generalized intervals will be
used for introducing a metric into the lattice of the
Fuzzy Interval Numbers (FINs) below.
The set of all positive generalized intervals of height
h is denoted by
h
+
M , the set of all negative
generalized intervals by
h
−
M , and the set of all
generalized intervals by M
h
.
2.1.1 The Basic Idea
FIN is constructed (see CALFIN algorithm below)
such that any horizontal line ε
h
, h∈ [0, 1], intersects
a FIN at exactly two points (only for h=1 there exists
a single intersection point). Hence, a horizontal line
ε
h
results in a “rectangular shaped pulse” of height h
which is called generalized interval of height h. If a
metric distance could be defined between every two
generalized intervals of height h then a metric
distance is implied by two FINs simple by
computing the corresponding definite integral from
h=0 to 1.
In figure 1 we can see two intersecting
generalized intervals [a’, c’], [b’, d’] at the height of
h. The intervals could be mapped to the conventional
intervals [a, c], [b, d]. The area “under” a
generalized interval is a real number which could be
calculated. We can define a metric distance and an
inclusion measure function in the set (lattice) of the
generalized intervals M
h
based on these notes.
2.1.2 The Lattice of All the Generalised
Intervals
Two functions are defined in the set of all the
generalized intervals M
h
:
1. Function support maps a generalized interval
to the corresponding conventional interval; support
([x
1
, x
2
]
h
) = [x
1
, x
2
] for positive, support([x
1
, x
2
]
h
) =
[x
2
, x
1
] for negative and support([x
1
, x
2
]
h
) = {x
1
} for
trivial generalized intervals.
2. Function sign: M
h
→ { –1, 0, +1 } maps a
positive generalized interval to +1, a negative
generalized interval to –1 and a trivial generalized
interval to 0.
A partial ordering relation ≤ can be defined in
the set M
h
, h∈ (0,1]:
1) [a, b]
h
≤ [c, d]
h
⇔ support([a, b]
h
) ⊆ support
([c, d]
h
), for [a, b]
h
, [c, d]
h
∈
h
+
M
⎩
⎨
⎧
≤≤
=
otherwise,0
,
)(
21
],[
21
xxxh
x
h
xx
μ
⎩
⎨
⎧
≥≥−
=
otherwise,0
,
)(
21
],[
21
xxxh
x
h
xx
μ
FUZZY INTERVAL NUMBER (FIN) TECHNIQUES FOR MULTILINGUAL AND CROSS LANGUAGE
INFORMATION RETRIEVAL
349

2) [a, b]
h
≤ [c, d]
h
⇔ support([c, d]
h
) ⊆
support([a, b]
h
), for [a, b]
h
, [c, d]
h
∈
h
−
M
3) [a, b]
h
≤ [c, d]
h
⇔ support([c, d]
h
) ∩
support([a, b]
h
) ≠ 0, for [a, b]
h
∈
h
−
M , [c, d]
h
∈
h
+
M
Kaburlazos
(Kaburlasos,2004) proved that the
set of all generalized intervals
M
h
is a lattice. If q
1
,
q
2
∈M
h
are the “intersecting” positive generalized
intervals [a’, c’]
h
, [b’, d’]
h
which are shown in
figure 1 then the join q
1
∨q
2
is equal to [a’, d’]
h
and
the meet q
1
∧q
2
is equal to [b’, c’]
h
. In a similar way
we can defined the meet and join in the case of non
intersecting positive, intersecting negative, and non
intersecting negative generalized intervals (Marinagi
et al, 2006).
2.1.3 A Metric Distance in the Set of All
Generalized Intervals M
h
A valuation v in a lattice L, defined as the area
“under” a generalized interval, is a real function v: L
→ R which satisfies v(x)+v(y)= v(x ∨
L
y) + v(x ∧
L
y), x,y ∈ L.A valuation is called monotone if and
only if x ≤ y implies v(x) ≤ v(y) and positive if and
only if x < y implies v(x) < v(y) for x,y ∈ L.
The role of a positive valuation function v: L→
R is to be a mapping from a lattice L of semantics to
the mathematical field R of real numbers for
carrying out computations.
Therefore a metric distance d:L×L→ R can be
defined in the lattice M
h
, h∈ (0,1] given by d(x, y)
= v(x ∨
L
y) - v(x ∧
L
y), x,y ∈ L.
Kaburlazos (Kaburlasos, 2004) proved the
following proposition:
Proposition
Let the underlying positive valuation function f:
R→ R be a strictly increasing real function in R.
Then the real function v: M
h
→ R is given by
v([a, b]
h
) = sign ([a, b]
h
) c(h)
a
∫
b
[f(x) – f(a)] dx
where v is a positive valuation function in M
h
, c:
(0,1] → R
+
is a positive real function for
normalization. A metric distance in M
h
is given by:
d
h
(x, y) = v(x ∨ y) – v(x ∧ y)
Therefore, we can choose a positive function f to
define the valuation function v and also simplify the
calculation of the distance between the generalized
intervals at a height h, 0≤h≤1 In the following
paragraphs we can see how to construct a strictly
increasing real function f
h
:R→R and simplify the
calculation of the definite integral. We can use an
integrable mass-function m
h
: R→
0
R
+
as: f
h
(x) =
∫
x
h
dttm
0
)(
.
Various mass functions can be considered:
For mass-function m
h
(x)=h it follows metric,
d
h
([a, b]
h
, [c, d]
h
)= h (|a-c|+|b-d|).
For mass-function m
h
(x)=3x
2
the corresponding
function is f
h
(x)=x
3
and therefore,
d
h
([-1,0]
1
, [3,4]
1
) = [f
h
(-1∨3) - f
h
(-1∧3)] +
[f
h
(0∨4) - f
h
(0∧4)] = 92.
If the mass function m
h
(x) is equal to,
2
2
(1 )
x
x
e
e
−
−
+
the corresponding (logistic) function is
f
h
(x) =
2
1
x
e
−
+
-1 =
1
1
x
x
e
e
−
−
−
+
and therefore,
d
h
([-1,0]
1
, [3,4]
1
) = [f
h
(-1∨3) - f
h
(-1∧3)] +
[f
h
(0∨4) - f
h
(0∧4)] = 2.3312.
3 FUZZY INTERVAL NUMBERS:
DEFINITION AND
COMPUTATION
Given a population (a vector of real numbers) x =
[x
1
,x
2
,…,x
N
] of measurements, sorted in ascending
order, a FIN can be computed by applying the
CALFIN algorithm below. FIN is regarded as an
abstract “mathematical object” and could have
various interpretations and uses. The notation dim(x)
denotes the dimension of vector x, e.g. dim([2,-1])=
2, dim([-3,4,0,-1,7])= 5, etc. The median(x) of a
vector x = [x
1
,x
2
,…,x
N
] is defined to be a number
such that half of the N numbers x
1
,x
2
,…,x
N
are
smaller than median(x) and the other half are larger
than median(x); for instance, the median([x
1
,x
2
,x
3
])
with x
1
< x
2
< x
3
equals x
2
, whereas the
median([x
1
,x
2
,x
3
,x
4
]) with x
1
< x
2
< x
3
< x
4
was
computed here as median([x
1
,x
2
,x
3
,x
4
])= (x
2
+ x
3
)/2.
Algorithm CALFIN
1. Let x be a vector of real numbers.
2. Order incrementally the numbers in vector
x.
3. Initially vector pts is empty.
4. function calfin(x) {
5. while (dim(x) ≠ 1)
6. medi:= median(x)
7. insert medi in vector pts
8. x_left:= elements in vector x less-than
number median(x)
9. x_right:= elements in vector x larger-than
number median(x)
ICEIS 2007 - International Conference on Enterprise Information Systems
350

10. calfin(x_left)
11. calfin(x_right)
12. endwhile
13. } //function calfin(x)
14. Sort vector pts incrementally.
15. Store in vector val, dim(pts)/2 numbers
from 0 up to 1 in steps of 2/dim(pts)
followed by another dim(pts)/2 numbers
from 1 down to 0 in steps of 2/dim(pts).
The above procedure is repeated recursively log
2
N
times, until “half vectors” are computed including a
single number; the latter numbers are, by definition,
median numbers. The computed median values are
stored (sorted) in vector pts whose entries constitute
the abscissae of a positive FIN’s membership
function; the corresponding ordinate values are
computed in vector val. Note that algorithm
CALFIN produces a positive FIN with a
membership function μ(x) such that μ(x)=1 for
exactly one number x.
Now we can focus on the transition from
document vectors to document FINs
FINs and documents’ representation and
construction
In the Vector Space Model for Information
Retrieval, a text document is represented by a vector
in a space of many dimensions, one for each
different term in the collection. In the simplest case,
the components of each vector are the frequencies of
the corresponding terms in the document:
Doc
k
= ( f
k1
, f
k2
, … f
kn
)
f
kj
stands for the frequency of occurrence of term
t
j
in document Doc
k
.
Example Table depicts the vector space model
of a small collection comprising four documents:
tf
kj
= tf
2,12
=6 stands for the frequency of
occurrence of term t
12
in document Doc
2
.
ctf
j
= ctf
12
=7 stands for the total frequency of
occurrence of term t
12
in the whole collection.
Then ctf
12
is equal to
1, 2 1,12 4,12
1,4
... 0 6 0 1 7
k
k
tf tf tf
=
=++=+++=
∑
The total frequency, of occurrence of term t
j
in the
whole collection, ctf
j
is equal to
kj
k
tf
∑
The collection term frequencies (ctf) are used as
term identifiers. In order to ensure the uniqueness of
the identifiers a multiple of a small ε is added to the
ctfs when needed (see last column of the table).
If we want to compute the FIN of the Doc1 then we
focus on the columns: Terms, Doc1, Term
Identifiers. We repeat the non-zero values of keys as
many times as the terms are contained in the Doc1.
In order to ensure the uniqueness of the identifiers
(keys) we add a multiple of a small ε to the ctfs
when needed again. The FIN of the document will
be computed from the identifiers of the terms that
exist in the document (19 values in our case).
Eventually, the abscissae vector is exactly the
“number population” from which the document FIN
is computed from by the CALFIN algorithm: X=
(4.333,4.334,4.335,4.667,4.668, 5, 5.25, 5.251,
5.252, 5.5, 5.501, 5.75, 6, 8, 8.001, 8.5, 8.501,
8.502,9). Figure 1 shows two FINs which are
calculated by our algorithm.
4 FUZZY INTERVAL NUMBERS
AND DOCUMENTS’
SIMILARITY
The FIN function F: (0,1] → M maps a real number
h in (0,1] to a generalized interval F(h). The domain
of function F is shown on the vertical axis, whereas
the range of function F includes “rectangular shaped
pulses” (generalized intervals at height h, h∈[0,1] )
on the plane. Now we can use the concepts we have
already seen in the case of the generalized intervals.
Hence, a positive Fuzzy Interval Number (FIN) is
defined as a continuous function F: (0,1] →
h
+
M
such that:
h
1
≤ h
2
⇒ support(F (h
1
)) ⊇ support(F( h
2
)), where
0 < h
1
≤ h
2
<1.
The set of all positive FINs is denoted by F
+
.
Similarly, negative FINs are defined. The set F of
FINs is partially ordered by an ordering relation
≤
F
,
which is introduced as follows:
Let F
1
, F
2
∈ F, then F
1
≤
F
F
2
if and only if
F
1
(h) ≤
M
h
F
2
(h)
The relation
≤
F
is reflexive, anti-symmetric and
transitive. There are incomparable FINs but we can
define the meet and the join, again. Using the metric
distance between two generalized intervals F
1
(h)
(=[a,b]
h
) and F
2
(h) (=[c,d]
h
) Kaburlasos
(Kaburlasos, 2004) proved the following
proposition:
Proposition
Given two positive FINs F
1
and F
2
, then
d(F
1
,F
2
)=
1
12
0
((), ())
h
cdFhFhdh
∫
where c is an user-defined positive constant, is a
metric distance
Calculation of the distance
FUZZY INTERVAL NUMBER (FIN) TECHNIQUES FOR MULTILINGUAL AND CROSS LANGUAGE
INFORMATION RETRIEVAL
351

0
0.25
0.5
0.75
1
a
a’
b
b’
c
c’ d’
d
h
Figure 1 illustrates how we calculate the distance
of two FINs F
1
and F
2
(representing two
documents) using a mass function. The points a, b,
c, d are used to define the distance, at height h,
d
h
(F
1
(h),F
2
(h)) = d
h
([a,b]
h
,[c,d]
h
); d
h
(F
1
(h),F
2
(h)) equals the
sum of areas of the shaded regions.
Hence, the distance of the two FINs at the height
h is given by the sum of areas of the shaded regions,
and eventually, the distance between the two FINs is
calculated using the definite integral of the distance
at height h from h=0 to 1:
Distance =
1' '
0' '
(| ( ) | | ( ) |)
bd
ac
f
tdt f tdt dh+
∫∫ ∫
The FIN distance is used instead of the similarity
measure between documents: the smaller the
distance the more similar the documents. For the
distance calculations a bell-shaped mass function
was used:
The positive real numbers ρ, σ, z, t, v are
parameters; maxctf is the maximum value of all the
collection term frequencies.
The mass function used was defined as tuning
result of our experimentation with the “similar” bell-
shaped mass function proposed by Marinagi et al.
We used visual representations of mass-functions in
order to study and improve our calculations. You
can see some examples of our experimentation with
such visual representations.
4.1 Illustrating Our Techniques by a
First Simplified Experiment
The retrieved documents were extracted from a
bibliographic database which is part of the Greek
National archive of Dissertations. Each document
(dissertation) is described by the title(s), the
abstract(s), the key-phrases etc. All these fields of
the description are bilingual: text in Greek and
English (or other language e.g. French).
Unfortunately, there are bibliographic descriptions
that are not complete e.g. abstracts are not included
in some cases. Our sample was constructed as the
union of the retrieved documents by two simple
queries (searches). More precisely, the documents of
the National archive of Dissertations were searched
using the (search) term “Natural Language
Processing” and retrieved seven documents. Five
documents contain the search term in their key-
phrases and the other documents contain the term in
other fields e.g. the abstract. Using the search term
h
2
(1 )
m(t)= [ (1 )]
1
11
/
h
t
zt
zmaxctf
ρ
ρ
σ
σ
ν
+−
+−
⎛⎞
−
+−
⎜⎟
⎝⎠
Figure 1: Two documents (vectors) illustrated as two Fuzzy Interval Numbers. Each value on the term axis represents a
term (stem). Given two FINs any “cut” at a given height h∈ (0,1] defines two generalized intervals, denoted by [a’, c’]
h
,
[b’, d’]
h
. In our case the generalized intervals are positive and intersecting. If you consider a “cut” at another height h=0.25
(∈ (0,1]) which defines a generalized interval denoted by F(h) or [b, d]
0.25
then the area [bd b’ d’] is the support (F(0.25))
where there are about 75% of the values. Calculation of the distance between two FINs (or the similarity between two
documents which are represented by their FINs). Use of a bell-shaped mass function for the calculations.
ICEIS 2007 - International Conference on Enterprise Information Systems
352

Table 1: Vector space model for a collection of documents.
terms ctf #docs Doc1 Doc2 Doc3 Doc4
Term identifiers
Term1 ctf
1
=3 2 tf
1 1
=0 tf
2 1
=1 tf
3 1
=0 tf
4 1
=2 3
Term2 ctf
2
=3 2 tf
1 2
=0 tf
2 2
=2 tf
3 2
=1 tf
4 2
=0 3,333
Term3 ctf
3
=3 3 tf
1 3
=0 tf
2 3
=1 tf
3 3
=1 tf
4 3
=1 3,667
Term4 ctf
4
=4 2 tf
1 4
=0 tf
2 4
=1 tf
3 4
=0 tf
4 4
=3 4
Term5 ctf
5
=4 2 tf
1 5
=3 tf
2 5
=0 tf
3 5
=1 tf
4 5
=0 4,333
Term6 ctf
6
=4 3 tf
1 6
=2 tf
2 6
=1 tf
3 6
=0 tf
4 6
=1 4,667
Term7 ctf
7
=5 3 tf
1 7
=1 tf
2 7
=3 tf
3 7
=0 tf
4 7
=1 5
Term8 ctf
8
=5 3 tf
1 8
=3 tf
2 8
=1 tf
3 8
=0 tf
4 8
=1 5.25
Term9 ctf
9
=5 3 tf
1 9
=2 tf
2 9
=0 tf
3 9
=2 tf
4 9
=1 5,5
Term10 ctf
10
=5 3 tf
1 10
=1 tf
2 10
=2 tf
3 10
=2 tf
4 10
=0 5,75
Term11 ctf
11
=6 3 tf
1 11
=1 tf
2 11
=0 tf
3 11
=2 tf
4 11
=3 6
Term12 ctf
12
=7 2 tf
1 12
=0 tf
2 12
=6 tf
3 12
=0 tf
4 12
=1 7
Term13 ctf
13
=8 4 tf
1 13
=2 tf
2 13
=2 tf
3 13
=1 tf
4 13
=3 8
Term14 ctf
14
=8 4 tf
1 14
=3 tf
2 14
=2 tf
3 14
=1 tf
4 14
=2 8,5
Term15 ctf
15
=9 3 tf
1 15
=1 tf
2 15
=4 tf
3 15
=0 tf
4 15
=4 9
“Information Retrieval” we retrieved twelve
documents and six of them contain the search term
in their key-phrases. The FIN-based calculation of
the distance (similarity) between the documents was
used.
Figure 2: Tuning of the mass function using visualization.
Description of the Classification technique
The results of the two searches form a parallel
corpus comprising two sets (classes) of documents
(dissertations):
{8011, 8432, 8433, 8728, 11137, 11646, 12648}
{909, 2792, 3015, 3171, 7781, 8553, 8556,
11143, 12197, 12424, 13239, 13290}
Then, we can form two sub-collections:
NLP-Collection = {8011, 8432, 8433, 11137,
12648}
IR-Collection = {3171, 8553, 8556, 11143,
12424, 13290}
These collections include only the documents
that contain the search term in their key-phrases. We
know that in the first NLP-Collection other two
documents are included: The document 8728 which
has a detailed (bibliographic) description, and the
document 11646 which has a shorter one (e.g. no
abstract is included).
We measure the distance of these two documents
from the two collections using the proposed FIN-
based technique and examine if the documents are
“closer” to the documents of the NLP-Collection.
Using a different terminology we use the two
collections as training sets and we try to classify
correctly the documents 8728, 11646. Then, we
must focus on the documents of the set {909, 2792,
3015, 7781, 12197, 13239} which are retrieved by
the second query and measure the distance of these
documents from the two collections using the
FUZZY INTERVAL NUMBER (FIN) TECHNIQUES FOR MULTILINGUAL AND CROSS LANGUAGE
INFORMATION RETRIEVAL
353

proposed FIN-based technique, and test if they have
to be classified in the NLP-collection.
We can calculate the average “matching” of the
document 8728 from the NLP-collection:
(0.105721+0.0670487+0.235943+0.213279+0.2565
49+0.240404)/6=0.1865
If we calculate the distance of the document
8728 from the IR-Collection = {3171, 8553, 8556,
11143, 12424, 13290} or the broader collection
{909, 2792, 3015, 3171, 7781, 8553, 8556, 11143,
12197, 12424, 13239, 13290} (and we can also use
or not stop-words file) we conclude that in general it
looks reasonable to add (classify) the document
8728 in the NLP-Collection. The details of the
experiment are summarized in the Table II below.
As you can see the document 11646 is correctly
classified in the NLP-Collection. We conclude that
the validity of the FIN-based calculation of the
similarity between documents is verified in the case
of the documents (dissertations) that are related to
the search term “natural language processing”.
Table 2: Experimental details.
Docume
nt
IR-
collection
NLP-
Collection
Comments
8728 0.3228 0.1865 Correct
11646 0.6788 0.2606 Correct
When the documents were restricted only in the
English part (e.g. title and abstract only in English)
the documents’ length was reduced and some
documents were erroneously classified. This was an
indication that our technique is more appropriate in
the case of “large” documents. In some cases there
was a need to “correct” the classification errors (and
in general improve our results) using a technique of
weights (or penalties or comparison with the top-x
“hits”) as we can see in Table III. Some
improvement could be also mentioned in the case of
adding in the collections “artificial” documents
(centroids) comprising elements of the documents.
We had also indications that such techniques will be
more useful in the case of bigger documents. The
same experiment was also conducted and all the
documents without abstracts were removed. Only
the documents {8011, 8432, 8433, 8728}, {909,
2792, 3015, 3171, 7781, 8553, 8556, 12197} were
used and we can report some improvement. Then we
“split” every document in two documents: the Greek
part of the document and the English one e.g.
8728Gre, 8728Eng. In table III we can see how we
can improve our classification results.
Table 3: Improved classification results.
Document
number
Distance
from IR-
collection
Distance
from NLP-
Collection
Comment
s
8728Gre 0.5889 0.1507
8728Eng 0.7551 0.1503
3171Gre 0.1088 0.1451 use of
weights
3171Eng 0.2904 0.2922 use of
weights
8553Gre 0.1773 0.2220 use of
weights
8553Eng 0.2141 0.3833
8553Eng <=0.0680 0.0776 use of the
top 4/5
“hits”
only
Table 4: Brief experimental corpora description.
Title and Author Lang
Common Sense Parenting (CSP) Learn
at home kit: A clinical effectiveness
evaluation of a commercially available video
training program for parents.
Sean T. Smitham, Ph.D., Western
Michigan University, 2004
English
The effects of group size on incentive
effectiveness: A meta-analysis.
Angelica C. Grindle, Ph.D., Western
Michigan University, 2002
English
A Market Analysis of consumer
behaviour for companies in a self-insurance
group, Bismarck J. Manes Jr., M.A.,
Western Michigan University, 2006
English
Refinement of temporal constraints in
an event recognition system using small
datasets, George Paliouras (NCSR
"Demokritos", Greece)
English
A Formal Semantics for the C
Programming Language, Nikolaos S.
Papaspyrou (National Technical University
of Athens)
English
Updating and retrieving information
through relational database views, Panayiota
Plessa (Ministry of Education, Greece)
Greek
The role of asynchronous hypermedia
conferencing in education and training, Cleo
Sgouropoulou (TEI of Athens)
Greek
Information system’s Security
management for coalitions in distributed
environments Petros Belsis (TEI of Athens)
Greek
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5 CONCLUSIONS
We have concluded that the FIN-based calculation
of the similarity between documents is a novel
method for solving various problems in the case of
CLIR-systems. We verified in our experiments that
if we focus on the documents (dissertations etc) that
are related to different search terms then we can
apply FIN-based techniques and calculate correctly
the distance (similarity) between documents.
Such techniques include the following cases:
1. Use partitions (“collections”) of the sample (e.g.
you can use the NLP-Collection and the IR-
Collection) and calculate the distance of the
(“unclassified”) document from such partitions
(“collections”). This distance is some kind of
average distance of the document from all the
elements of the collection.
2. Use partitions (“collections”) of the sample, and
define the number of “hits” (e.g. top-4 or top-5).
Calculate the distances of the (“unclassified”)
document from the (e.g. four or five) documents
of each partition. Use only the (top-x) documents
which are closer to the unclassified one. Then
you can calculate the average distance of the
unclassified document from these top-x
documents.
3. Use increased weights for the search terms that
are contained in the retrieved documents.
4. Use of positive weights in the case that the
search term is included in a document of the
partition and use of penalty (negative weight) in
the case that the search term is not included.
5. If the length of the documents is greater then the
results of the method are better.
A strategy related to the specific sample of
classified and unclassified documents could be
defined. As an example, you can combine 1 & 2
and if it is necessary weights and penalty: If the
general (average) distance of a document from a
collection (which is calculated from the
distances of the document from all the classified
documents of the collection) and the partial one
(which is calculated from a number of the top-x
distances) are not “consistent” you must use
weights following the appropriate technique.
ACKNOWLEDGEMENTS
This work was co-funded by 75% from the
European Social Fund and 25% by National
Resources (EPEAEK-II)-Archimedes.
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