https://doi.org/10.31449/inf.v44i3.2996 Informatica 44 (2020) 387–393 387
How to DefineCo-occurrence in a Multidisciplinary Context?
Mathieu Roche
CIRAD, TETIS, F-34398 Montpellier, France
TETIS, Univ. Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France
E-mail: mathieu.roche@cirad.fr,http://textmining.biz/Staff/Roche
Position paper
Keywords: co-occurrence, collocation, phrase,n-gram, skyp-n-gram, association rule, sequential pattern
Received: October 28, 2019
This position paper presents a comparative study of co-occurrences. Some similarities and differences
in the definition exist depending on the research domain (e.g. linguistics, natural language processing,
computer science). This paper discusses these points and deals with the methodological aspects in order to
identify co-occurrences in a multidisciplinary paradigm.
Povzetek: Predstavljena je analiza soˇ casnosti.
1 Introduction
Determining co-occurrences in corpora is challenging for
different applications such as classification, translation, ter-
minology building, etc. More generally, co-occurrences
can be identified with all types of data, e.g. databases [8],
texts [30], images [38], music [15], video [19], etc.
The co-occurrence concept has different definitions de-
pending on the research domain (i.e. linguistics, natu-
ral language processing (NLP), computer science, biology,
etc.). This position paper reviews the main definitions in
the literature and discusses similarities and differences ac-
cording to the domains. This type of study can be crucial
in the context of data science, which is geared towards de-
veloping a multidisciplinary paradigm for data processing
and analysis, especially textual data.
Here the co-occurrence concept related to textual data is
discussed. Note that before their validation by an expert,
co-occurrences of words are often considered as candidate
terms.
First, Section 2 of this paper details the different defini-
tions of co-occurrence according to the studied domains.
Section 3 discusses and compares these different aspects
based on their intrinsic definition but also on the associated
methodologies in order to identify them. Finally, Section 4
lists some perspectives.
2 Co-occurrence in a
multidisciplinary context
2.1 Linguistic viewpoint
In linguistics, one notion that is broadly used to define the
term is called lexical unit [23] and polylexical expression
[16]. The latter represents a set of words having an au-
tonomous existence, which is also called multi-word ex-
pression [33].
In addition, several linguistics studies use the collocation
notion. [10] gives two properties defining a collocation.
First, collocation is defined as a group of words having an
overall meaning that is deducible from the units (words).
For example, climate change is considered as a collocation
because the overall meaning of this group of words can be
deduced from both words climate and change. On the other
hand, the expression to rain cats and dogs is not a colloca-
tion because its meaning cannot be deduced from each of
the words; this is called a fixed expression or an idiom.
A second property is added by [10] to define a colloca-
tion. The meaning of the words that make up the collo-
cation must be limited. For example, buy a dog is not a
collocation because the meaning of buy is not limited.
2.2 NLP viewpoint
In the natural language processing (NLP) domain, the co-
occurrence notion refers to the general phenomenon where
words are present together in the same context. More pre-
cisely, several principles are used that take contextual cri-
teria into account.
First, the terms or phrases [6, 11] can respect syntactic
patterns (e.g. adjective noun, noun noun, noun preposition
noun, etc.). Some examples of extracted phrases (i.e. syn-
tactic co-occurrences) are given in Table 1.
In addition, the methods without linguistic filtering are
also conventionally used in the NLP domain by extracting
n-grams of words (i.e. lexical co-occurrences) [25, 35].n-
grams are contiguous sequences ofn words extracted from
a given sequence of text (e.g. the bi-grams
1
x y and y z
are associated with the textxyz).n-grams that allow gaps
1
n-grams withn = 2.

388 Informatica 44 (2020) 387–393 M. Roche
are called skip-n-grams (e.g. the skip-bi-grams x y, x z,
y z are related to the text x y z). Skip-gram model is an
efficient method for learning high-quality distributed vec-
tor representations that capture a large number of precise
syntactic and semantic word relationships [27]. Some ex-
amples ofn-grams and skip-n-grams are given in Table 1.
After summarizing the term notion in the NLP domain,
the following section discusses these aspects in the com-
puter science context, particularly in data mining. Note that
the NLP domain may be considered as being located at the
linguistics and computer science interface.
2.3 Computer science viewpoint
In the data mining domain, co-occurring items are called
association rules [1, 39] and they could be candidates for
construction or enrichment of terminologies [12].
In the data mining context, the list of items corresponds
to the set of available articles. With textual data, items may
represent the words present in sentences, paragraphs, or
documents [2, 29]. A transaction is a set of items. A set
of transactions is a learning set used to determine associa-
tion rules.
Some extensions of association rules are called sequen-
tial patterns. They take into account a certain order of ex-
tracted elements [18, 34] with an enriched representation
related to textual data as follows:
– objects represent texts or pieces of texts,
– items are the words of a text,
– itemsets represent sets of words present together
within a sentence, paragraph or document,
– dates highlight the order of sentences within a text.
There are several algorithms for discovering associa-
tion rules and sequential patterns. One of the most pop-
ular is Apriori, which is used to extract frequent itemsets
from large databases. The Apriori algorithm [1] finds fre-
quent itemsets wherek-itemsets are used to generatek +1-
itemsets.
Association rules and sequential patterns of words are
often used in text mining for different applications, e.g. ter-
minology enrichment [12], association of concept instances
[5, 29], classification [18, 34], etc.
3 Discussion: comparative study of
definitions and approaches
This section proposes a comparison of: (i) co-occurrence
definitions (see Section 3.1), (ii) automatic methods in or-
der to identify them (see Section 3.2). This section high-
lights some similarities and differences between domains.
3.1 Co-occurrence extraction
The general definition of co-occurrence is finally close to
association rules in data mining domain. Note that the in-
tegration of windows
2
in the association rule or sequential
pattern extraction process enables us to have similarity with
skip-n-gram extraction.
The integration of syntactic criteria makes it possi-
ble to extract more relevant candidate terms (see Table
1). Such information is typically taken into account in
NLP to extract terms from general or specialized domains
[20, 24, 28, 32].
Table 1 highlights relevant terms extracted using linguis-
tic patterns (e.g. climate change, water cycle, significant
change). The use of linguistic patterns tends to improve
precision values. Generally other methods such as skip-
bi-grams return lower precision, i.e. many extracted can-
didates are irrelevant (e.g. climate the). But this kind of
method enables extraction of some relevant terms not found
with linguistic patterns (e.g. cycle expected); then the recall
can be improved.
Table 2 presents research domains related to different
types of candidates, i.e. collocations, polylexical expres-
sions, phrases, n-grams, association rules, sequential pat-
terns.
Table 3 summarizes the main criteria described in the
literature. Note that the extraction is more flexible and au-
tomatic when there are fewer criteria. In this table, two
types of information are associated with the different crite-
ria. The first one (marked withX) designates the character-
istics given by the co-occurrence definitions. The second
type of information (marked withF) represents character-
istics that are implemented in many extensions of the state-
of-the-art.
Table 3 shows that the semantic criterion is seldom as-
sociated with co-occurrence definitions. This criterion is
however taken into account in linguistics. For example,
semantic aspects are taken into account in several studies
[17, 22, 26]. In this context [26] introduced lexical func-
tions rely on semantic criteria to define the relationships
between collocation units. For instance, a given relation
can be expressed in various ways between the arguments
and their values, like Centr (the center, culmination of) that
returns different meanings
3
:
– Centr(crisis) = the peak
– Centr(desert) = the heart
– Centr(forest) = the thick
– Centr(glory) = summit
– Centr(life) = prime
In the data mining domain, semantic information is used
in two main directions. The first one involves filtering the
2
Association Rule with Time-Windows (ARTW) [39].
3
http://people.brandeis.edu/ smalamud/ling130/lex_functions.pdf

How to Define Co-occurrence in. . . Informatica 44 (2020) 387–393 389
Sentence (input)
With climate change the water cycle is expected to undergo significant change.
Candidates (output)
Phrases climate change
(noun noun, adjective noun) water cycle, significant change
bi-grams of words With climate, climate change, change the, the water,
water cycle, cycle is, is expected, expected to,
to undergo, undergo significant, significant change
2-skip-bi-grams With climate, With change, With the,
climate change, climate the, climate water,
change the, change water, change cycle,
the water, the cycle, the is,
water cycle, water is, water expected,
cycle is, cycle expected, cycle to,
is expected, is to, is undergo,
expected to, expected undergo, expected significant,
to undergo, to significant, to change,
undergo significant, undergo change,
significant change
Table 1: Examples of candidates extracted with different NLP techniques.
Definitions Domains
Collocations L
Polylexical expressions L + NLP
Phrases NLP
n-grams NLP + CS
Association rules CS
Sequential patterns CS
Table 2: Summary of the main domains associated with
expressions (L: linguistics, NLP: natural language process-
ing, CS: computer science).
results if they respect certain semantic information (e.g.
phrases or patterns where a word is an instance of a seman-
tic resource). Other methods involve semantic resources
in the knowledge discovery process, i.e. the extraction is
driven by semantic information [5].
In recent studies in the NLP domain, the semantic as-
pects are based on word embedding, which provides a
dense representation of words and their relative meanings
[14, 40].
Finally, note that several types of co-occurrence are of-
ten used in different domains. For example, polylexical
expressions are commonly used in NLP and also in lin-
guistics. In addition, n-grams is currently used in NLP
and computer science domains. For example, n-grams of
words are often used to build terminologies (NLP domain)
but also as features for machine learning algorithms (com-
puter science domain) [35].
Table 4 summarizes the main types of criteria (i.e. statis-
tic, morpho-syntactic, and semantic) used for extracting co-
occurrences according to the research domains considered
in this paper.
After presenting the characteristics associated with the
co-occurrence notion in a multidisciplinary context, the
following section compares the methodological viewpoints
to identify these elements according to the domains.
3.2 Ranking ofco-occurrences
Co-occurrence identification by automatic systems is gen-
erally based on the use of quality measures and/or algo-
rithms. This section provides two illustrative examples that
show similarities between approaches according the do-
mains.
3.2.1 MutualInformation andLiftmeasure
Firstly the use of specific statistical measures from differ-
ent domains is highlighted. This subsection focuses on the
study of Mutual Information (MI). This measure is often
used in the NLP domain to measure the association be-
tween words [9]. MI (see formula (3.1)) compares the
probability of observing x and y together (joint probabil-

390 Informatica 44 (2020) 387–393 M. Roche
Ordered Sequences Morpho-syntactic Semantic
sequences with gaps information information
Collocations X X F
Polylexical expressions X X
Phrases X X
n-grams X F
Association rules X
Sequential patterns X X
Table 3: Summary of the main criteria associated with co-occurrence identification. X represents the respect of the
criterion by definition.F is present when extensions are currently used in the state-of-the-art.
Statistic Morpho-syntactic Semantic
information information information
Linguistics X F
NLP X X F
Data mining X F F
Table 4: Summary of the main criteria associated with research domains. X represents the respect of the criterion for
extracting co-occurrences from textual data.F is present when extensions are currently used in the state-of-the-art.
ity) with the probability of observingx andy independently
(chance) [9].
I(x) =log
2
P (x;y)
P (x)P (y)
(3.1)
In general, word probabilities P (x) and P (y) corre-
spond to the number of observations ofx andy in a corpus
normalized by the size of the corpus. Some extensions of
MI are also proposed. The algorithm PMI-IR (Pointwise
Mutual Information and Information Retrieval) described
in [36] queries the Web via the AltaVista search engine to
determine appropriate synonyms for a given query. For a
given word, denotedx, PMI-IR chooses a synonym among
a given list. These selected terms, denoted y
i
, i2 [1;n],
correspond to TOEFL questions. The aim is to compute
they
i
synonym that gives the best score. To obtain scores,
PMI-IR uses several measures based on the proportion of
documents where both terms are present. Turney’s formula
is given below (3.2): It is one of the basic measures used
in [36]. It is inspired from MI described in [9]. With this
formula (3.2), the proportion of documents containing both
x andy
i
(within a 10 word window) is calculated and com-
pared with the number of documents containing the word
y
i
. The higher this proportion, the morex andy
i
are seen
as synonyms.
score(y
i
) =
nb(xNEARy
i
)
nb(y
i
)
(3.2)
– nb(x) computes the number of documents containing
the word x (i.e. nb corresponds to number of web-
pages returned by search engines),
– NEAR (used in the ’advanced research’ field of Al-
taVista) is an operator that identifies if two words are
present in a 10 word wide window.
This kind of web mining approach is also used in
many NLP applications, e.g. (i) computing the relation-
ship between host and clinical sign for an epidemiology
surveillance system [3], (ii) computing the dependency of
words of acronym definitions for word-sense disambigua-
tion tasks [31].
The probabilities are generally symmetric (i.e.
P (x;y) = P (y;x)), while the original MI measure
is also symmetric. But the association ratio applied in
the NLP domain is not symmetric, i.e. the occurrence
number of pairs of words "xy" and "yx" generally differ.
Moreover the meaning and relevance of phrases should
differ according to the word order in a text, e.g. first lady
and lady first.
Finally, MI is very close to the lift measure [7, 37, 4] in
data mining. This measure identifies relevant association
rules (see formula (3.3)). The lift measure evaluates the
relevance of co-occurrences only (not implication) and how
x andy are independent [4].
lift(x!y) =
conf(x!y)
sup(y)
(3.3)
This measure is based on both confidence and support
criteria, which in turn are based on association rule (x!y)
identification. Support is an indication of how frequently
the itemset appears in the dataset. Confidence is a standard
measure that estimates the probability of observingy given
x (see formula (3.4))
conf(x!y) =
sup(x[y)
sup(x)
(3.4)
Note that other quality measures of the data mining do-
main, such as Least contradiction or Conviction [21], could

How to Define Co-occurrence in. . . Informatica 44 (2020) 387–393 391
be tailored to deal with textual data.
3.2.2 C-value andcloseditemset
Another example is the methodological similarities associ-
ated with different approaches. For example, the C-value
approach [13] used in the NLP domain [24, 20] favors
terms that do not appear to a significant extent in longer
terms. For example, in a specialized corpus related to oph-
thalmology, the work of [13] shows that a more general
term such as soft contact is irrelevant, whereas a longer
and therefore more specific term such as soft contact lens
is relevant. This kind of measure is particularly relevant in
the biology domain [24, 20].
In addition, in the computer science domain (i.e. data
mining), the notion of closed itemset is finally very close to
the C-value approach. In this context, a frequent itemset is
considered as closed if none of its supersets
4
has the same
support (i.e. frequency).
This section and both illustrative examples confirm the
importance of having a real multidisciplinary viewpoint
on the methodological aspects in order to build scien-
tific bridges and thus contribute to the development of the
emerging data science domain.
4 Conclusion and Future Work
This position paper proposes a discussion on similarities as
well as differences in the definition of co-occurrence ac-
cording to research domains (i.e. linguistics, NLP, com-
puter science). The aim of this position paper is to show
the bridges that exist between different domains.
In addition, this paper highlights some similarities in the
methodologies used in order to identify co-occurrences in
different domains. We could extend the discussion to other
domains. For example, methodological transfers are cur-
rently applied between bioinformatics and NLP. For exam-
ple, the use of edition measures (e.g. Levenshtein distance)
for sequence alignment tasks (bioinformatics) v.s. string
comparison (NLP).
Acknowledgments
This work is funded by the SONGES project (Occitanie
and FEDER) – Heterogeneous Data Science (http://
textmining.biz/Projects/Songes).
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