Proceedings of the Second ACL Workshop on Effective Tools and Methodologies for Teaching NLP and CL, pages 57–61,
Ann Arbor, June 2005. c©2005 Association for Computational Linguistics
Teaching Language Technology at the North-West University 
 
Suléne Pilon Gerhard B van Huyssteen Bertus van Rooy 
sktsp@puk.ac.za ntlgbvh@puk.ac.za ntlajvr@puk.ac.za 
Research Focus Area: Languages and Literature in the South African Context North-West University, 
Potchefstroom  
2531 
 South Africa  
   
Abstract 
The BA Language Technology program 
was recently introduced at the North-West 
University and is, to date, the only of its 
kind in South Africa. This paper gives an 
overview of the program, which consists 
of computational linguistic subjects as 
well as subjects from languages, computer 
science, mathematics, and statistics. A 
brief discussion of the content of the pro-
gram and specifically the computational 
linguistics subjects, illustrates that the BA 
Language Technology program is a voca-
tionally directed, future oriented teaching 
program, preparing students for both fu-
ture graduate studies and a career in lan-
guage technology. By means of an 
example, it is then illustrated how stu-
dents and researchers alike benefit from 
working side by side on research and de-
velopment projects by using a problem-
based, project-organized approach to cur-
riculum design and teaching.  
1 Introduction 
A new undergraduate teaching program, BA Lan-
guage Technology, was recently introduced at the 
Potchefstroom Campus of the North-West Univer-
sity (NWU). The introduction of this program was 
motivated by two factors: 
(a) a need within the Faculty of Arts to develop 
teaching programs that are relevant, vocationally 
directed, and future-oriented; and 
(b) a need in the South African higher education 
system for capacity building in the field of in lan-
guage technology (PanSALB & DACST, 2000). 
To date, the BA Language Technology program 
is the only one of its kind in South Africa. It has 
therefore remained imperative that the program 
equips students adequately to fill positions in the 
emerging South African language technology in-
dustry. At the same time, students should be able 
to continue with graduate studies, and therefore the 
program had to be designed in such a way that stu-
dents receive an academic training that incorpo-
rates a solid theoretical component alongside the 
need to get enough practical experience. These two 
imperatives are reflected in the program structure, 
and also in the project-based learning approach 
that we adopted. 
2 Program Structure  
After wide consultation with international and lo-
cal role players and experts, a program was de-
signed that combines language subjects and natural 
sciences (mainly computer science, mathematics 
and statistics) with a core group of computational 
linguistic and language technology subjects. This 
section offers an overview of the BA Language 
Technology program. An example of a typical pro-
gram will be given and the modules which form 
part of the program will be discussed briefly. 
The program has a basic core of compulsory 
modules, but allows some room for students to 
take modules based on personal interest and abil-
ity. A student who excels in computer program-
ming can choose to take additional modules from 
that field after completing the compulsory mod-
ules. Students may also choose to take more lan-
guage or mathematics modules after completing 
their compulsory modules. There are also a number 
of general formative subjects that all students at the 
University must take, which are not being dis-
cussed here. The basic course structure is presented 
in Table 1.   
57
Table 1: BA Language Technology compulsory modules with choices 
 
The various general formative modules offered at 
the university include academic literacy, study 
skills, computer literacy and information skills, 
philosophy and academic and scientific writing 
courses. The elective modules from which the stu-
dents can choose are mathematics, computer sci-
ence and languages. The languages from which the 
students can choose are Afrikaans, English or 
Setswana (regular university courses) or introduc-
tory courses (foreign language level) in two South 
African languages, Setswana and isiZulu and two 
foreign European languages, German and French.  
Students are encouraged to take at least one 
South African language. This is motivated in part 
by trends in the macro-political environment. In 
government policy documents, such as the final 
language policy presented to cabinet, language 
technology is principally regarded as a means to 
promote multilingualism and increase access of 
information in a country with eleven official lan-
guages. In the context of the program itself, it is 
expected that students acquire and/or improve their 
proficiency in the various languages; students are 
also expected to develop basic knowledge of the 
structure of the particular languages. This basic 
knowledge is then developed further in the module 
“Linguistics for language technology students” 
(second year, first semester). The module includes 
components of phonetics, morphology and syntax, 
to enable students to learn how to do detailed lin-
guistic data analysis.  
In the first semester of the second year, students 
are introduced to Language Technology. An over-
view of the field of study is given and it is indi-
cated how the knowledge students gained in the 
modules they have completed, should be put to use 
within the field. The course also focuses on the 
relationship between a more practical language 
technology orientation and a more theoretical natu-
ral language processing (NLP) orientation, to en-
able students to see the broader picture and 
develop a sense for the coherence of the teaching 
program.  
Language technologies are the subject of two 
modules in the second semester of the third year. 
They spend equal amounts of time on speech-
based technologies and text-based technologies. 
The focus of these courses is specific language 
technology applications. At any given time, there 
are a number of ongoing projects at the university. 
Students are involved in these projects, learning to 
develop the specific applications, but also develop-
ing general skills for other types of applications, 
within the framework of project-based learning, as 
will be outlined later in this paper. Students are 
expected to participate in ongoing projects on vari-
ous levels, ranging from annotating corpora to in-
tricate programming – depending on their aptitude 
and preferences.  
This is followed by a six-month internship in the 
first semester of the fourth year, at an approved 
company or higher education or research institu-
tion. Apart from extending their training in the de-
velopment of language technology applications, 
the internship is intended to let students get a “real 
YEAR 1 YEAR 2 YEAR 3 YEAR 4 
First semester First semester First semester First semester 
Modules Modules Modules Modules 
Computer Science  
(programming) 
Language Technology: 
Introduction 
Introduction to NLP Language technology: 
Internship 
2 Languages 1 Language 1 CHOICE  
Statistics (introduction) Computer Science  
(programming) 
2 General formative mod-
ules 
 
Mathematics 1 CHOICE   
Applied Mathematics    
2 General formative modules    
Second semester Second semester Second semester Second semester 
Modules Modules Modules Modules 
Computer Science  
(programming) 
Language Technology: 
Linguistics for language 
technology students 
Language Technology: 
Speech applications 
Advanced NLP 
1 Language 1 CHOICE Language Technology: 
Text applications 
Language Technology 
Project 
Statistics (Inferential) 2 General formative mod-
ules 
1 CHOICE  
1 CHOICE    
58
world” experience in the language technology in-
dustry before they have to make career decisions.  
In their final semester, students have to com-
plete a supervised project, which fits in with cur-
rent research at the university. It is important that 
students should be positive about this project and 
therefore students are consulted when project to-
pics are chosen. In this stage of the program, stu-
dents have very little class in order to enable them 
to work on their projects on a full-time base, which 
provides for more practical experience. 
Students are introduced to Natural Language 
Processing in the first semester of the third year. 
This course focuses mainly on statistical tech-
niques for the analysis of the kinds of phonetic, 
morphological and syntactic data that were intro-
duced in the second semester of the second year. 
The logic is that students must be able to analyze 
data manually as linguists first, in order to develop 
an appreciation for the capabilities, power and 
limitations of statistical NLP methods.  
An advanced NLP course is offered in the se-
cond semester after students have completed their 
internship and while they are working on their own 
projects. This course is tailored to the individual 
interests and needs of the students. The specific 
NLP techniques relevant to their projects, as well 
as problems they encountered during their intern-
ships, serve as guiding principles for the selection 
of content. At the same time, we incorporate a se-
lection of hot topics in NLP research and some 
techniques for dealing with semantic data. 
As computational linguistics is a relatively new 
field of study in South Africa, students and lectur-
ers/researchers have to learn together, even by 
making mistakes and taking ‘wrong’ sidetracks 
during the learning process. In order to facilitate 
these circumstances, a problem-oriented and pro-
ject-organized approach, based on the educational 
system developed at the Aalborg University, Den-
mark, since 1974 (Kjersdam & Enemark, 1994), 
was taken in the design of the curricula of the Lan-
guage Technology and NLP modules. This means 
that the content of some of the Language Technol-
ogy and NLP subjects vary from year to year, de-
pending on the current project(s) being conducted 
at the university. However, by working alongside 
each other on research and development projects, 
both students and lecturers engage in active learn-
ing, proving to yield excellent results in the acquir-
ing of knowledge in the field. The next section 
describes how various research and development 
projects are integrated in the undergraduate and 
graduate teaching programs, in order to facilitate 
hands-on, outcome-based learning.  
3  Teaching Approach: Problem-Based, 
Project-Organized Learning 
Problem-Based Learning (PBL; also called pro-
blem-oriented education) can be defined as learn-
ing “based on working with unsolved, relevant and 
current problems from society/real life… By ana-
lyzing the problems in depth the students learn and 
use the disciplines and theories which are consid-
ered to be necessary to solve the problems posed, 
i.e. the problem defines the subjects and not the 
reverse” (Kjersdam & Enemark, 1994: 16; cf. 
Schwartz et al., 2001; Macdonald, 2002). This ap-
proach is successfully implemented world-wide in 
the teaching of specifically more applied sciences, 
such as, inter alia, medicine (Albanese & Mitchell, 
1993; Barrows and Tamblyn, 1980; Moore et al., 
1994), and engineering (De Graaf & Kolmos, 
2003; Fink, 2002). 
Within the context of computational linguistics, 
this "applied-teaching approach" maintains a dy-
namic triangular equilibrium between training, re-
search and product development, serving 
researchers, students, and the industry alike. A pro-
ject-organized approach offers lecturers an oppor-
tunity to align course material with their research 
projects, while students are enabled to gain “com-
prehensive knowledge of the development of theo-
retical and methodological tools” (Kjersdam and 
Enemark, 1994: 17). Therefore, after completion of 
their formal studies, students should be able to 
contribute to research and the development of 
original paradigms to solve new and complex 
problems in the future. 
In the BA Language Technology program, PBL 
is incorporated with project-organized education in 
two ways. On the one hand various project-based 
modules are included in the curriculum. For in-
stance in the third year of study, the modules 
“Language Technology: Speech Applications” and 
“Language Technology: Text Applications” are 
introduced, where students have to develop various 
small modules for both speech and text technologi-
cal applications (e.g. a simple rule-based stemmer). 
In the final year of study, the largest part of the 
year is spent on independent project work, which is 
59
conducted either at the university, or while doing 
an internship elsewhere. These projects are on a 
much larger scale than the third year projects, with 
the possibility to build on the work of the previous 
year (e.g. to develop a more sophisticated stemmer, 
using more advanced NLP techniques).  
On the other hand, some of the other modules 
(e.g. the “Natural Language Processing” modules) 
are more project-driven, since they are organized 
around existing research projects. Students are 
mostly drawn in on a so-called “design-oriented” 
level, i.e. where they have to deal with “know-how 
problems which can be solved by theories and 
knowledge they have acquired in their lectures” 
(Kjersdam & Enemark, 1994: 7). After the project 
and the problems related to the project are ex-
plained to students, they get involved by collecting 
data, identifying possible/different solutions, for-
mulating rules and algorithms, analyzing data, 
evaluating different components, etc. In this way 
they get know-how and experience in theoretical, 
methodological, and implementation issues. 
This can be illustrated by a recent example, 
where work on a spelling checker project was inte-
grated in the curricula of various modules. In this 
project, involving the development of spelling 
checkers for five different South African lan-
guages, a variety of NLP techniques were imple-
mented in the various spelling checkers, depending 
on the orthographical complexity of and resources 
available for a specific language. For instance, lan-
guages such as Tswana and Northern Sotho have a 
relatively simple orthographical structure (in the 
sense that it is more disjunctive), and a straight-
forward lexicon-based approach to spelling check-
ing therefore suffice for these languages. In 
contrast, Afrikaans, Zulu and Xhosa are ortho-
graphically more complex languages, requiring a 
spelling checking approach based on morphologi-
cal analysis or decomposition, which is of course 
more interesting from a computational linguistic 
perspective. For all of these languages, almost no 
resources were available at the start of the project, 
posing a huge but interesting challenge (e.g. could 
available technologies for other languages, such as 
a Porter stemmer, be adapted for these lan-
guages?). 
From the onset of the spelling checker project, 
students were involved in all aspects of the project. 
Using Jurafsky & Martin (2000) as a point of de-
parture, students were introduced to the basic pro-
blems of spelling checking, relating it to the 
current project and specifically to the challenges 
posed by spelling checking for Afrikaans (e.g. pro-
ductive concatenative compound formation, deri-
vational word formation, etc.). Students were 
thoroughly involved in all discussions of the aims 
of the project, potential problems and possible so-
lutions, as well as the general system architecture 
(i.e. students were involved on the design-oriented 
level). Students were therefore introduced to basic 
concepts such as tokenization, stemming, and 
Levenshtein Distance (for purposes of generating 
suggestions), within a real-world context.  
 After the planning and design phase, each stu-
dent got involved in solving different problems of 
the project, e.g. developing a stemmer (using fi-
nite-state techniques) and a compound analyzer 
(using machine-learning techniques), the automatic 
generation of a lexicon, evaluating spelling check-
ers (within the broader context of the evaluation of 
NLP applications), etc. Although each student 
worked separately on different problems, they 
were forced to extend their experience by helping 
each other with their different tasks, thereby ex-
panding their general knowledge and experience. 
In this way, students also came to learn that differ-
ent problems call for different approaches: to use 
finite-state techniques for hyphenation in Afri-
kaans is simply to labor-intensive, while machine 
learning offers highly efficient solutions to the 
problem. An introduction to machine learning was 
therefore also introduced in the curriculum. 
The advantages of this approach proved to be 
many: not only did the project benefit from the 
sub-projects of each of the students, but students 
got the feeling that they were involved in “impor-
tant” and relevant work. They got the opportunity 
to apply the theoretical knowledge they acquired in 
the classes in a practical, hands-on environment, to 
improve their understanding of the theories and 
concepts of the study field, and to solve real-world 
problems. Additionally, members of staff were 
enabled to harmonize their research and teaching 
responsibilities, optimizing the quality and quantity 
of their outputs. Moreover, existing students were 
motivated to continue with their studies in compu-
tational linguistics on MA level (where they are 
working on more advanced problems), while un-
dergraduate student numbers increased (which can 
be ascribed to a greater awareness of language 
technology in the community, brought about par-
60
tially by media coverage of the project, focusing 
on the promotion of multilingualism and language 
empowerment). 
4 Conclusion 
Since its very inception, the BA Language Tech-
nology program at the North-West University was 
designed as a vocationally directed, future-oriented 
teaching program. A curriculum with a core of 
computational linguistic subjects, strengthened by 
a strong foundation in languages, computer sci-
ence, mathematics, and statistics, equips students 
both with enough practical experience to start 
working in the industry, and with enough theoreti-
cal knowledge to continue with postgraduate stu-
dies.  
By taking a problem-based, project-organized 
approach to curriculum design, students and re-
searchers alike benefit from working side by side 
on research and development projects (as illustra-
ted by the incorporation of a spelling checker pro-
ject in the curricula of various subjects). The same 
approach is followed in other subjects, such as 
"Language Technology: Speech Applications", 
where students are working in collaboration with 
their lecturers on various speech-based projects. As 
new research projects are initiated, the curricula of 
the various subjects are adapted accordingly. For 
example, in 2005 a new research project on syntac-
tic parsing commenced – consequently, new stu-
dents are confronted with other problems than their 
predecessors, while still learning, for example, 
about the differences between linguistic and statis-
tical approaches to NLP. With the help of students 
in the program and others involved, the program is 
constantly evaluated and adjusted accordingly, 
thereby ensuring that it delivers well-educated and 
informed students, prepared for the challenges of a 
career in language technology. 

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