A Lexico-semantic Approach to the Structuring of Terminology 
Marie-Claude L’HOMME 
OLST – Université de Montréal 
C.P. 6128, succ. Centre-ville 
Montréal (Québec), Canada H3C 3J7 
Marie-Claude.L’Homme@umontreal.ca 
http://www.olst.umontreal.ca 
 
Abstract 
This paper discusses a number of implications 
of using either a conceptual approach or a 
lexico-semantic approach to terminology 
structuring, especially for interpreting data 
supplied by corpora for the purpose of 
building specialized dictionaries. A simple 
example, i.e., program, will serve as a basis 
for showing how relationships between terms 
are captured in both approaches. My aim is to 
demonstrate that truly conceptual approaches 
do not allow a flexible integration of terms 
and relationships between terms and that 
lexico-semantic approaches are more 
compatible with data gathered from corpora. I 
will also discuss some of the implications 
these approaches have for computational 
terminology and other corpus-based 
terminological endeavours. 
 
1 Introduction 
Recent literature in terminology circles 
constantly reminds us that methods and practices 
have changed drastically due mostly to the 
extensive use of electronic corpora and computer 
applications. What might appear as normal and 
standard in computational circles has had profound 
consequences for terminologists; this has led many 
to criticize traditional theoretical principles and 
some to propose new approaches (Bourigault and 
Slodzian 1999; Cabré, 2003, among others; see 
L’Homme et al., 2003 for a review). 
One of the issues at the centre of this debate is 
that of diverging views on the relationship between 
the term and the abstract entity it is supposed to 
represent (a “concept” or a “meaning”). Differing 
views will inevitably lead to very different ways of 
envisaging terms and methods of structuring them. 
Some might be compatible with a given 
application, while others are much more difficult to 
accommodate. 
In this paper, I will try to demonstrate some of 
the methodological consequences of adopting a 
conceptual approach or a lexico-semantic approach 
to terminology structuring. These observations are 
drawn from my experience in compiling 
specialized dictionaries using corpora as primary 
sources and computer applications to exploit them.  
Even though the application I am familiar with is 
very specific and obviously influences my view on 
the structuring of terms, I believe this topic is also 
relevant for other terminology-related applications. 
For example, in computational terminology, there 
is an increasing interest for structuring extracted 
terms (articles in Daille et al., 2004 and in 
Nazarenko and Hamon, 2002, among others). 
Automatic term structuration is carried out by 
considering morphological variants (Daille, 2001; 
Grabar and Zweigenbaum, 2004), performing 
distributional analysis to build classes of 
semantically related terms (Nazarenko et al., 2001, 
among others), or acquiring other types of 
linguistic units, such as collocations or verbal 
phrases, from specialized corpora. 
These questions will be addressed from a 
linguistic point of view, but many have been dealt 
with directly or indirectly by computational 
terminologists and, in fact, are often raised by their 
work on specialized corpora. I will also try to 
demonstrate that the problems dealt with in this 
paper are by no means a reflection of a tendency 
often attributed to linguists to make things more 
complicated than they actually are. I would like to 
show that they are a reflection of the functioning of 
terms in running text. 
2 Two different approaches to terminology 
The conceptual approach I describe is the one 
advocated by the Vienna School of terminology 
that has been and is still applied to work carried 
out by terminologists. The results of its analyses is 
encoded in term records in term banks or in articles 
in terminological dictionaries.  
CompuTerm 2004  -  3rd International Workshop on Computational Terminology 7
The lexico-semantic approach on which my 
discussion is based is the Explanatory and 
Combinatorial Lexicology (ECL) (Mel’èuk et al., 
1995; Mel’èuk et al. 1984-1999) which is the 
lexicological component of the Meaning-text 
Theory (MTT). As will be seen further, ECL 
provides an apparatus, namely lexical functions 
(LFs), that can capture a wide variety of semantic 
relations between lexical units. ECL descriptions 
are encoded in an Explanatory and Combinatorial 
Dictionary (ECD) (Mel’èuk et al. 1984-1999). 
In order to illustrate the methodological 
consequences of the two approaches under 
consideration, I will use a basic term in the field of 
computing, i.e., program. This term was chosen 
because no one will question its status in 
computing no matter what his or her view is on 
terms and terminology.  
In addition, like many basic terms, program is 
polysemic, ambiguous in some contexts, and 
semantically related to several other terms. It will 
be very useful to show the variety of semantic 
relationships in which terminological units 
participate. Finally, program does not refer to a 
concrete object. Hence, its analysis will pose 
problems different from those raised by terms like 
printer or computer. 
I will also frequently refer to a corpus from 
which my observations are derived. This corpus 
contains over 53 different texts and amounts to 
600,000 words. It was compiled by the 
terminology team within the group Observatoire 
de linguistique Sens-Texte (OLST) in Montreal. 
Since I am not an expert in computer science, I 
must rely – like other terminologists – on 
information provided in a corpus and not on 
previous knowledge to analyze the meaning of 
program and the other terms to which it is related. 
2.1 A conceptual approach to the processing 
of the term program 
When considering a unit such a program, 
terminologists who adhere to a conceptual 
approach will define its place within a conceptual 
structure. This is done by considering  its 
characteristics (in fact, often by deciding which 
ones are relevant), and by analyzing classical 
relationships, such as hyperonymy (or, rather, 
generic-specific) and meronymy (or whole-part). 
In order to achieve this, terminologists usually 
gather information from reliable corpora. 
The corpus first informs us that “program” can 
be subdivided into in one of the following 
categories; 1. “operating system”; 2. “application 
software”, i.e., “word processor”, “spreadsheet”, 
“desktop publishing software”, “browser”, etc.; 
and 3. “utility program”. It also tells us that there 
are different types of “programs”: 1. “shareware 
programs”, “freeware programs”; “educational 
programs”; and “commercial programs”; 2. 
“command-driven programs” and “menu-driven 
programs”. 
One possible representation of these 
relationships has been reproduced in Figure 1. Of 
course, my interpretation of the data listed above is 
simplified, since it does not take into account all 
the relationships that can be inferred from it (e.g., 
the fact that software programs or educational 
programs can be menu-driven). Also, part-whole 
relationships for some of these subdivisions can be 
identifed (e.g., the fact that programs – classified 
according to the interface – have parts such as 
menus, windows, buttons, options, etc.).  
 
program 
 
according to the task or tasks to perform   
 
  operating system 
 
  application software 
 
   word processor 
   spreadsheet 
   desktop publishing software 
   browser 
 
  utility program 
 
 according to the  interface 
 
  command-driven program 
  menu-driven program 
 
 according to the market 
  
  shareware program 
  freeware program 
  commercial program 
  educational program 
Figure 1: Representation of the relationships 
between “program” and related concepts 
For the time being, I will assume that I have 
solved the problems related to the relations 
between “program” and other relevant concepts 
(which, in fact, is not the case, as we will see 
below). 
The corpus also allows me to observe that the 
concept I am currently dealing with, has different 
names: program and software program. This will 
normally be dealt with in conceptual 
CompuTerm 2004  -  3rd International Workshop on Computational Terminology8
representations by taking for granted that all these 
different linguistic forms refer to the same concept, 
and thus are true synonyms. In my representation, 
they will be attached to the same node as 
“program” (see Figure 2).1 
Furthermore, since concepts and conceptual 
representations are considered to be language-
independent, their description and representation 
should be valid for all languages. Hence, my 
representation system should apply to French (and 
to true synonyms in French) and other languages 
(see Figure 2). 
 
 program (program; software program) 
      (Fr. logiciel) 
 
  according to the task or tasks to perform   
 
    application software (application 
                          software; application) 
                         (Fr. logiciel d’application; 
                         application) 
 
                        … 
 
Figure 2: Synonyms in conceptual 
representations 
Regarding this last issue, a choice must often be 
made between several potential synonyms in order 
to select a single identifier for a concept. This 
choice can simply be functional (allowing the 
labelling of a node in a representation such as that 
in Figure 1) or result from standardizing efforts. 
The choice of a unique identifier is central in 
conceptual analyses, since relationships are defined 
first and foremost between concepts and are 
considered to be valid for the linguistic forms that 
label them. 
2.2 Other issues related to the analysis of 
program  
In my discussion on the processing of program, I 
deliberately avoided other important issues 
revealed by the data contained in the corpus. We 
will look at some of these issues in this section. 
First, “programs” can be further classified 
according to the language used create them (“C 
programs”, “C++ programs”, “Java programs”), or 
according to the hardware device they manage 
                                                 
1Large-scale ontologies represent concepts and 
lexical forms using a similar strategy. For example, the 
Unified Medical Language System (UMLS) (National 
Library of Medicine, 2004) makes a clear separation 
between a Semantic Network and a Lexicon. 
(“BIOS program”, “boot program”). Incidentally, 
in French, the first subdivision (the one represented 
in section 2.1) corresponds to logiciel. The ones we 
just introduced are named programme. 
This obviously has consequences for the 
representation of program produced above. The 
problem can be solved in conceptual approaches 
by: 
a. Considering that program refers to a single 
concept, and trying to account for the 
different ways of organizing its relationships 
with other concepts with new conceptual 
subdivisions. This will produce a very 
complex, yet possible, graphical 
representation; 
b. Focussing on a single organization of the 
concept “program” (for example, the one 
chosen in section 2.1.) and defining the 
others as being related to vague or improper 
uses of program; or, finally,  
c. Saying that program is associated with two 
or three different concepts, and possibly 
classifying them into three different 
subfields of computing, i.e., concept1 = 
micro-computing; concept2 = programming; 
concept3 = hardware. If the description is 
carried out in a multilingual context, the 
subdivision will be necessary to account for 
the fact that, in French, for instance, 
program can be translated by logiciel or 
programme. This latter choice is the one that 
is closest to the distinctions made with the 
lexico-semantic approach dealt with in the 
following section. 
 Secondly, program shares with other lexical 
units many other different semantic relationships 
other than the taxonomic and meronymic relations 
previously considered. All the relationships listed 
below have been found in the corpus.2  
o Relationships that involve activities and that 
are expressed linguistically mostly by 
collocates of program: 
Function: a program performs tasks  
Creation: development, creation of a 
program, programming 
Actions that can be carried out on programs: 
configuration, installation, running, 
aborting, etc. 
                                                 
2Some of these have been listed in Sager (1990) who 
argued that a large variety of conceptual relationships 
could be found in specialized subject fields. 
 
CompuTerm 2004  -  3rd International Workshop on Computational Terminology 9
o Relationships that involve properties and that 
are also expressed linguistically by 
collocates of program: 
powerful program, user-friendly program; 
feature of a program  
o Argument or circumstantial relationships: 
Agent: user of a program; programmer  
Instrument: create a program with a 
language 
Location: install the program on the hard 
disk, on the computer  
o Other relationships expressed by 
morphological derivatives terms that include 
the meaning of program; 
programming, programmable, 
reprogrammable 
 
 Most relationships listed above are non-
hierarchical and may be expressed by parts of 
speech other than nouns. Consider, for example, 
actions that can be performed on a program 
(configuration, configure; install; installation, 
etc.). 3 Some will be very difficult to account for in 
terms of conceptual representations. Of course, 
conceptual-approach advocates might argue that 
these relationships are not relevant for 
terminology. 
Thirdly, in my discussion of the fact that 
concepts could have different names, I mentioned 
only a synonym, but concepts are expressed in a 
variety of forms in corpora. Many of these will not 
take the form of nouns. 
2.3 A lexico-semantic approach 
In this section, I repeat my analysis of program 
this time using a lexico-semantic approach. This 
approach is also based on data gathered from 
corpora. The discussion presented in this section is 
summarized in Table 1. 
First, the analysis of program in the corpus 
reveals that it has three different meanings. 
Program can be defined as: 1) a set of instructions 
written by a programmer in a given programming 
language in order to solve a problem (this meaning 
is also conveyed by computer program); 2) a set of 
programs (in sense 1) a user installs and runs on 
his computer to perform a number of tasks (this 
meaning being also conveyed by software 
program); and 3) a small set of instructions 
designed to run a specific piece of hardware. 
                                                 
3 Another non-hierarchical relationship has received a 
lot of attention recently, that of cause-effect. 
This sense distinction is validated by the fact that 
program can be related to different series of lexical 
units.  
For example, a program1 is something that 
someone, called a programmer, writes, executes, 
compiles and debugs. It can be machine-readable 
or human-readable. It can also end or terminate.  
Program can be modified by names given to 
languages, i.e., C program, C++ program, Java 
program. Finally, it can also have parts such as 
modules, routines, and instructions. 
 
Program1  
Explanation Set of instructions written by a 
programmer in a programming 
language to solve a specific problem 
Collocates write ~; compile ~, execute ~; create 
~; machine-readable ~; human-
readable ~; ~ ends, ~ terminates, 
debug ~; powerful ~ 
Hyponyms C ~, C++  ~, Java ~ 
Other 
related 
terms 
to program; programming, 
programmer; routine, instruction; 
module; page; segment; language; 
line 
Program2  
Explanation Set of programs1 installed and run on 
the computer by a user to perform a 
specific task or a set of related tasks. 
Hyponyms operating system; application 
software; word processor, 
spreadsheet 
Collocates active ~, running of ~; download ~; 
develop ~; run ~, install ~; uninstall 
~; add/remove ~;  user-friendly; quit 
~; exit ~; load ~; launch ~ 
Other 
related 
terms 
user, hard disk;  
application  
software 
Program3  
Explanation Short set of specific instructions 
designed to run a hardware device 
Other 
related 
terms 
boot, BIOS, to program, reprogram, 
programmable, reprogrammable, 
programming 
Table 1: Semantic distinctions for program 
A program2 is something a user installs on his 
computer, loads into the memory, runs, and 
sometimes uninstalls.  Different sorts of programs 
can be identified, such as operating systems, 
applications, and utilities. Programs can have parts 
such as windows, menus, options, etc. Finally, a 
program2 can be user-friendly. 
CompuTerm 2004  -  3rd International Workshop on Computational Terminology10
A program3 consists of a few code lines written 
in order to specify the behaviour of a specific 
hardware device, such as a memory. The device is 
then said to be programmable and/or 
reprogrammable. It can be programmed and 
reprogrammed.  
In this lexico-semantic approach, the 
relationships observed between program and other 
terms are attached to its specific meanings. This 
distinction allows us to relate other terms to 
specific senses. For example, program1 is related 
to other senses as follows: 
Synonym: computer ~ 
Types of programs: C ~, Java ~ 
Parts of programs: instruction, page, segment, 
line, routine 
Creation of a program: write ~, create ~, to 
program, programming 
Agent: programmer 
Cause a program to function: execute ~ 
The program stops functioning: ~ ends, ~ 
terminates 
etc. 
Since most semantic relationships are non-
hierarchical, they can be represented in a relational 
model. In ECL, paradigmatic and syntagmatic 
semantic relations are represented by means of a 
single formalism, i.e., lexical functions (LFs). LFs 
are used to capture abstract and general senses that 
remain valid for a large number of lexical units. 
The relationships listed above could be formalized 
as follows: 4  
synonym: Syn(program1) = computer ~ 
agent of a program: S1(program1) = programmer 
create a program: CausFunc0(program1) = 
create [DET ~], write [DET ~] 
Cause a program to function:                                                
CausFact0(program1) = execute [DET ~] 
The program stops functioning: 
FinFact0(program1) = [DET ~] ends, [DET ~] 
terminates 
                                                 
4Meronymic and hyperonymic relationships can also 
can also be captured by means of lexical functions. 
Authors have proposed LFs especially designed to 
represent these relations (Spec, for hyponymy; and 
Part; for meronymy). However, ECL will prefer 
accounting for these relationships with non-standard 
lexical functions in order to explain the specific nature 
of the relationships between a lexical unit and its 
meronym. 
3 General comments on the analyses of terms 
These two brief analyses of program reveal the 
following about terms: 
• Terms can convey multiple meanings. This 
is not an accidental property that only affects 
program. Numerous examples can be found 
in corpora and have been dealt with in recent 
literature. This, of course, has important 
consequences for both conceptual and 
lexico-semantic approaches.  
• Terms can enter into a large variety of 
relationships with other terms, and not only 
taxonomic or meronymic relationships. The 
understanding of these relationships is 
necessary to capture sense distinctions; in 
addition, relationships are valid for a specific 
meaning. 
• Some of the relationships observed between 
terms are hierarchical: hyperonymy and 
meronymy. 
• Most semantic relationships are non-
hierarchical: e.g., actions carried out by 
terms, properties, cause-effect. 
• Some relationships involve lexical units 
other than nouns: e.g., actions and creation 
are often expressed linguistically by means 
of verbs; properties are expressed by 
adjectives. 
• Most relationships involve terms considered 
as linguistics units rather than labels for 
concepts: e.g., morphological derivatives. 
In fact, what these observations tend to show is 
that terms behave like other lexical units and must 
be dealt with accordingly. Terms will acquire their 
specificity through a given application with set 
objectives, but as units occurring in corpora, terms 
cannot be differentiated from other lexical units. 
4 Implications for computational 
terminology and other corpus-based work 
The previous discussion has a number of 
implications for computational terminology (as 
well as other corpus-based terminology-related 
applications). I will examine a few in this section. 
First, both approaches will focus on different 
types of units when selecting terms in corpora. In 
conceptual approaches, a selection is made among 
linguistic units that can refer to a concept. The 
focus is on nouns and noun phrases. Even though 
concepts can be expressed in a variety of linguistic 
forms, synonyms considered will invariably be 
nouns of noun phrases. Work on terminological 
CompuTerm 2004  -  3rd International Workshop on Computational Terminology 11
variation (Daille, 2003; Jacquemin, 2001) has 
shown the variety of forms that terms can take in 
corpora (morphological derivation, insertion, 
elision, anaphora, etc.), but these are taken into 
account only if they can be associated with an 
admitted term.  
In a lexico-semantic approach as that presented 
in section 2.3, units considered will be those that 
convey a meaning that can be related to the field of 
computing (the subject field is delimited prior to 
the selection). Lexical units selected can pertain to 
different parts of speech as long as their meaning 
can be related to the field under examination: 
nouns (program, byte); verbs (debug, to program), 
adjectives (user-friendly, programmable). Even 
adverbs can convey a specialized meaning (e.g., 
digitally, dynamically). 
Secondly, any terminological work based on 
corpora will run into polysemy, even though it 
focuses on a small set of terms. The manner in 
which the distinctions between senses are made 
has important consequences on way terms will be 
processed afterwards.  
Polysemy can be dealt with using a conceptual 
approach, which considers this property to be an 
accidental problem. Hence, distinctions depend on 
decisions made during the classification process or 
the construction of conceptual representations.  
In lexico-semantic approaches, polysemy is 
viewed as a natural property of lexical units. 
Senses are delimited prior to the representation of 
semantic relationships and this delimitation is 
based on the observations of interactions between 
the term under examination and other lexical units. 
Sense delimitation and distinction is a necessary 
step before anything else can be done. 
Thirdly, regarding terminology structuring, 
conceptual methods, such as the one discussed in 
section 2.1, are useful as far as classification is 
concerned. Hence, they can be used for describing 
concepts that correspond to entities (concrete 
objects, substances, artefacts, animates, etc.). 
Moreover, the focus is on hierarchical relations 
(hyperonymy and meronymy) which is again valid 
for entities, and, as far as part-whole relations are 
concerned, more specifically concrete objects. 
Many non-hierarchical relationships, such as those 
listed in section 2.3 are disregarded, either because 
they involve units that do not refer to entities, or 
because they are relationships between lexical 
units and not concepts.  
Also, relationships between synonyms are 
considered from the point of view of true 
synonymy. Choosing a unique linguistic identifier 
for a concept and considering competing linguistic 
forms as true synonyms has implications for the 
variety of relationships that can be considered. 
Some relationships can be valid for one synonym 
but not for another. 
In lexico-semantic approaches, semantic 
relationships are attached to senses that have been  
distinguished previously. In addition, a wide 
variety of semantic relationships can be taken into 
account. These relationships can apply to terms 
that designate entities, as well as activities, and 
properties. Hypernymy and meronymy represent 
only a small part of the semantic relationships 
terms can share with other terms. Other 
relationships, such as argument relations, entity-
activity relations, can be expressed by different 
parts of speech.  
Fourthly, conceptual approaches lead to 
representations that distance themselves from data 
collected in corpora. Many decisions are made 
during the construction of the representation. On 
the one hand, many meanings that would appear to 
be relevant in other approaches are not considered. 
On the other hand, things are added in order to 
build the representation. Consider, for example, 
Figure 1. Some subdivisions are created but do not 
correspond to lexical units (e.g., according to the 
interface); this sort of classification of units will 
result in considering several complex sequences 
that have a compositional meaning (hence, that are 
not true lexical units). 
Terminology structuring in conceptual 
approaches is often carried out in order to represent 
knowledge and not linguistic units. Problems arise 
when this work is done using corpora as a starting 
point, since linguistic units (such as terms) do not 
behave in a way that reflects perfectly a given 
knowledge structure. When analyzing terms, 
considerations regarding knowledge structure will 
constantly interfere with factors related to the 
behaviour of linguistic units in text. 
On the other hand, lexico-semantic approaches 
are much more compatible with data gathered from 
corpora. Of course, terminologists will make 
decisions since they must interpret data and 
synthesize their findings, but these are based on the 
observation of interactions between lexical units 
that appear in corpora. 
5 Concluding remarks 
The point in my discussion, is not to say that an 
approach is much better than the other for 
terminology, regardless of the application at hand. 
This topic has been dealt with extensively by 
authors and even placed in a theoretical 
CompuTerm 2004  -  3rd International Workshop on Computational Terminology12
perspective. Rather, I wanted to demonstrate that 
an approach is probably better suited that the other 
as far as terms considered in corpora are 
concerned. I also wanted to point out the 
methodological consequences of choosing an 
approach over another. 
Conceptual approaches will  account for 
consensual representations of knowledge, based on 
a predefined set of hierarchical relationships. 
However, in must be kept in mind that resulting 
representations distance themselves from corpus 
data and necessitate a lot of hand-crafted changes. 
Often, the ideal knowledge structure is formulated  
beforehand entirely or partly, and the difficulty 
consists in trying to find lexical units that fit into it. 
Lexico-semantic approaches will provide 
terminologists with a framework for interpreting 
data related to terms and the contexts in which they 
appear. However, one must accept, when using this 
kind of approach, that terminological structures are 
discovered gradually through semantic relations 
and that some of these relations will even 
contradict assumed knowledge structures.  
6 Acknowledgements 
I would like to thank Elizabeth Marshman for 
her comments on a preliminary version of this 
article. 

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