CL for CALL in the Primary School 
Katrina Keogh, Thomas Koller, Monica Ward,  
Elaine Uí Dhonnchadha , Josef  van Genabith 
School of Computing 
Dublin City University 
Dublin 9, Ireland 
{kkeogh, tkoller, mward}@computing.dcu.ie,  
Elaine.UiDhonnchadha@dcu.ie, josef@computing.dcu.ie 
 
Abstract 
This paper looks at how Computational 
Linguistics (CL) and Natural Language Processing 
(NLP) resources can be deployed in Computer-
Assisted Language Learning (CALL) materials for 
primary school learners.  We draw a broad 
distinction between CL and NLP technology and 
briefly review the use of CL/NLP in e-Learning in 
general, how it has been deployed in CALL to date 
and specifically in the primary school context.  We 
outline how CL/NLP resources can be used in a 
project to teach Irish and German to primary 
school children in Ireland. This paper focuses on 
the use of Finite State morphological analysis 
(FST) resources for Irish and Part of Speech (POS) 
taggers for German. 
1 Introduction 
CL/NLP has a lot to offer many disciplines. One 
particular area of interest is e-Learning for 
languages or more specifically Computer-Assisted 
Language Learning (CALL). CALL aims to 
develop useful learning tools with the focus on the 
learner. The following sections outline the use of 
CL/NLP in CALL (also known as Intelligent 
Computer-Assisted Language Learning - ICALL) 
for a particular target audience – primary school 
students in Ireland.  
First we review CL/NLP in e-Learning and the 
case for using CL/NLP in CALL. Next we describe 
ICALL and the case for its use in primary school. 
Section 4 goes into detail on the CL/NLP 
technologies we use for primary school students 
learning Irish and German. 
2 CL/NLP in e-Learning 
2.1 CL/NLP – A Broad Distinction 
To a first approximation CL/NLP technologies 
split into two broad categories – A and B. Category 
A (sometimes referred to as CL proper) typically 
includes small coverage, proof of concept, often 
hand-crafted, knowledge- or rule-based systems. 
They are usually used to test a particular linguistic 
theory, tend to be of limited coverage and are often 
quite brittle. Example technologies include DCGs 
and many (but not all) formal grammar-based 
parsing and generation systems. 
Category B (sometimes referred to as NLP) 
typically includes broad coverage systems where 
the lingware is often (but not always – see e.g. 
FST) automatically induced and processed using 
statistical approaches. They are usually large scale 
engineering applications and very robust. Example 
technologies include speech processing, HMM 
taggers, probabilistic parsing and FST. 
This distinction is, of course, nothing more than 
a useful over-generalisation with an entire and 
interesting grey area existing between the two 
extremes. Khader et al. (2004), for example, show 
how a wide-coverage, robust rule-based system is 
used in CALL. In this paper we look at the 
suitability of type A and B CL/NLP technologies 
for primary school education, in the context of 
Ireland in particular. 
2.2 e-Learning  
CL is generally not to the fore in e-Learning, 
although it does have a potentially powerful role to 
play.  It can help to enhance the accessibility of 
online teaching material (particularly when the 
material is not in the learner’s L1), in analysing 
learner input and the automatic generation of 
simple feedback. It can also be used with 
Computer-Mediated Communication (CMC) 
environments.  However, to date, the use of 
CL/NLP in e-Learning in general has not been a 
main stream focus of either the Computational 
Linguistics or the e-Learning community nor has 
there been much CL/NLP technology transfer into 
commercially available and deployed systems.  
2.3 CALL 
Within the domain of e-Learning, the area with 
the greatest fit and potential deployment of 
CL/NLP resources is that of Computer-Assisted 
Language Learning (CALL). This paper focuses on 
asynchronous e-Learning for natural languages in 
the primary school context. CL/NLP resources 
lend themselves naturally to the domain of 
language learning, given that the “raw material” in 
both fields is language. However, attempts to 
successfully marry the two fields have been 
limited.  Schulze (2003) outlines several reasons 
for this.  Computational Linguists are specifically 
interested in the use of the computer in analysing, 
generating and processing language.  They are 
interested in testing out linguistic theories and 
using the computer to confirm their hypotheses. 
Researchers in NLP tend to be interested in wide-
coverage, robust engineering approaches. For the 
most part, use of their tools for language 
learning/teaching applications is not high on their 
research agenda.  A review of COLING papers in 
the last twenty years reveals that there are very few 
papers that specifically deal with the use of 
CL/NLP in language learning.  Furthermore, as 
Schulze (2003) points out, within the unspoken 
hierarchy that exists in Computer Science 
departments throughout the world, working with 
CALL is considered less prestigious than say, 
working on cryptography. Thus, socio-cultural 
factors may have played a part in limiting the 
number of CL/NLP researchers interested in 
CALL. 
From a CALL researcher’s or practitioner’s 
point of view, attempts to integrate CL/NLP 
resources into CALL have not been very 
successful. Many remain unconvinced about the 
benefits of using CL/NLP techniques in CALL and 
whether they can be integrated successfully or not.  
They sometimes expect an ‘all-singing, all-
dancing’ machine and are disappointed 
/disillusioned with the results of ICALL research, 
especially when they incorporate category A CL 
technologies. CALL practitioners generally come 
from a language teaching background and are often 
more interested in pedagogy than technology.  
Some feel that the technical knowledge required to 
integrate CL/NLP tools is beyond their scope. 
They may be wary of claims from CL/NLP 
developers that a certain CL/NLP resource will be 
“ideal” for CALL, especially if they have heard 
such claims before.  Even if they are favourably 
disposed to the use of CL/NLP resources in CALL, 
it is often very difficult to reuse existing resources, 
as they demand that a certain (often non-standard) 
format be used for data (see Sections 4.2 and 5.2 
below).  Also, the interfaces to the resources may 
have assumed a techno-savvy or CL/NLP-savvy 
user, which mitigates against their (re)use.   
In summary, apart from notable exceptions (e.g. 
Glosser (Dokter & Nerbonne, 1998) and FreeText 
(2001), for various technical and non-technical 
reasons, CL/NLP resources have not been 
extensively deployed in main-stream CALL 
applications.  
 
One of the problems in using CL/NLP resources 
in CALL materials is that the coverage achieved by 
the CL/NLP tools has to be broad to be able to 
handle a general range of learner language. 
Furthermore, the resources must be robust as 
learner language will contain input that is not well-
formed and this can cause problems for some CL 
resources. Observations such as these point to type 
B NLP technologies as being the better type of 
technologies to employ in the context of language 
learning. However, below we argue that this is not 
necessarily the case. 
2.4 ICALL in the Primary School 
It may be natural to assume that CL/NLP 
resources customarily lend themselves to 
intermediate or advanced learners of a language, as 
they are more likely to have the linguistic 
competence to understand output generated by 
CL/NLP resources. Considering the other end of 
the language-learning spectrum, that of primary 
school learners, it may be perceived that CL/NLP 
resources could not be so easily deployed with 
linguistically less advanced learners - these 
students will not be interested in viewing 
concordances, morphological annotations or parse 
trees.  
However, it can be argued that there are certain 
natural circumstances supporting the use of even 
type A CL technology in CALL in this 
environment.  Firstly, in comparison to adults, 
young learners have limited first language (L1) 
performance (Brown, 1994). The target primary 
school students are aged between 7 and 13 years 
(second to sixth class in the Irish primary school 
system). They tend to produce simpler sentences 
and have a smaller range of vocabulary than an 
adult. These L1 features have a number of 
implications – the students’ L1 knowledge further 
constrains their emerging L2 production. Complex 
linguistic constructs are less likely to transfer into 
the target language. Effectively, the target 
language amounts to a controlled language. 
Controlled languages are easier suited to type A 
CL systems and produce better results (Arnold et 
al., 1994).  
Secondly, the students’ target language(s) (Irish 
and German in this context) represent a limited 
domain or sublanguage. The Irish curriculum is 
followed in primary schools from the age of 4/5. 
Students can take German (where it’s available) 
during their senior years of primary school (aged 
10-13) and the language domain is limited to a 2 
year beginners’ curriculum. It is possible to 
anticipate students’ L2 knowledge, especially since 
they have been following set curricula. Machine 
Translation (MT) can be used to highlight an 
example of the success of sublanguages with 
CL/NLP. The Météo translation system is used 
successfully in Canada to translate weather 
forecasts bi-directionally between French and 
English (Hutchins and Somers, 1992). The 
‘weather’ sublanguage has a small vocabulary and 
uses a telegraphic style of writing and omits tense.  
Primary school students’ L1 and L2 performance 
characteristics – controlled language and limited 
domain – imply that some scalability problems that 
are sometimes encountered in certain type A CL 
resources can be avoided. 
While primary school learners will not be 
interested in viewing concordances or parse trees – 
technology can be used but hidden from the 
learner, to generate exercises and learner feedback 
and to present students with an animation based on 
information computed by the underlying CL/NLP 
engines embedded (but not visible) in the CALL 
application. In this way the learner will benefit 
from the technologies but not be confused by 
linguistic elements that are beyond their capacity 
as young learners.  
3 CL/NLP Resources for CALL 
In this paper we look at how CL/NLP resources 
can be integrated into CALL materials in general, 
as well as specifically for Primary Schools in 
Ireland, with a focus on CALL materials for Irish 
and German. This section will briefly outline how 
a range of CL/NLP resources can be used in this 
environment, while later sections will focus on the 
use of specific CL/NLP resources in more detail.  
We return to our dichotomy of A- and B-type 
CL/NLP systems outlined in Section 2.1. ICALL 
systems have used a range of technologies, 
including both type A and type B systems. 
Examples of type A-like systems include small-
scale Lexical Functional Grammar (LFG) –based 
robust parsers to provide error recognition and 
feedback (Reuer, 2003) and parsing for viewing 
sentence structures and error diagnosis 
(Vandeventer Faltin, 2003). Examples of type B-
like systems include a broad-coverage English 
LFG-based grammar for grammar checking 
(Khader et al, 2004), the Systran MT system to 
improve translation skills (La Torre, 1999) and 
using speech recognition for pronunciation training 
(Menzel et al, 2001).  
It is relatively straightforward to integrate type B 
(NLP) technology into CALL applications for 
primary school learners. In Section 4 of this paper 
we show how broad-coverage FST technology can 
be used to morphologically analyse word forms or 
to generate all inflected forms given a root form. 
Output from a FST morphology engine is fed into 
an interface engine which sends the information in 
the appropriate format to an XML/Flash 
environment for animation (Koller, 2004). The 
learner input can be collated over time into a 
learner corpus and later analysed by the teacher to 
detect common errors amongst students.  Part-Of-
Speech (POS) taggers can be used to identify the 
parts of speech in electronic versions of learners’ 
textbooks or a corpus collated around their 
curriculum (Section 5).  The output can then be 
used for a variety of uses, including the automatic 
generation of online exercises (e.g. hangman) and 
together with the FST morphological engine - 
automatic dictionary extraction. 
Mainly due to scalability problems, type A CL 
technologies can be difficult to deploy in general 
ICALL systems. However, they can be used in the 
primary school context quite effectively. As 
outlined in Section 2.4, the limited linguistic 
performance knowledge of the learners’ L1 and 
especially their L2 amounts to a ‘controlled’ 
language scenario and type A CL technologies can 
be deployed successfully. Curricula used in 
primary schools (in Ireland and elsewhere) 
represent a limited domain in which type A 
technologies can be highly appropriate. Small 
coverage DCGs, for example, can be written for 
the anticipated L2 learner input and can be used to 
provide immediate feedback to the learner. 
Problems associated with difficulties in building 
wider-coverage grammars do not present 
themselves in this context, as the curriculum is 
limited. 
The are many other potential uses of CL/NLP in 
this context, but this paper will focus on the FST 
and POS tagging examples mentioned above.  
4 CL/NLP Resources for Irish Primary 
School CALL  
4.1 Background 
Irish is a compulsory subject in schools in 
Ireland.  Students generally tend to have a negative 
attitude towards the language, which hinders 
learning (Harris & Murtagh, 1999).  Until recently, 
Irish has been taught using the Audio-Lingual 
method (structural patterns are taught using 
repetitive drills) and it is only since 1999 that a 
new communicative curriculum (language teaching 
is structured around topics in terms of 
communicative situations) has been developed and 
integrated. Currently, there are very few CALL 
resources available for Irish (Hetherington, 2000) 
and those that do exist may not be as error-free as 
one would like, are not specifically aimed at 
primary school learners and are therefore not tied 
to the Primary School curriculum which hinders 
their integration into the classroom. 
4.2 A FST-Based Morphological Engine for 
Irish 
Uí Dhonnchadha (2002) has developed an 
analyser and generator for Irish inflectional 
morphology using Finite-State Transducers 
(Beesley and Karttunen, 2003). The FST engine 
contains approximately 5,000 lexical stems, 
generates/recognises over 50,000 unique inflected 
surface forms with a total of almost 400,000 
morphological descriptions (due to ambiguous 
surface forms). The final FST is the result of 
composing intermediate transducers, each 
encoding a different morphological process. It is 
useful to have a record of the morphological 
processes involved in mapping between lexical 
(i.e. lemmas and morphological features) and 
surface forms. By including a marker in the surface 
form each time a process is applied, a record of the 
morphological processes involved can be 
maintained and used in other applications. 
The morphological processes covered include: 
(i) internal mutations such as lenition, ellipsis, 
stem internal modification and vocal prefixing; (ii) 
final mutation, such as vowel harmony with 
suffixes (broadening, slenderising and 
syncopation); as well as concatenative morphology 
(prefixing, suffixing). 
4.3 Technology - FST, Perl, XML and Flash 
Primary school learners are not interested in 
viewing output generated by a FST Morphology 
engine. The challenge in CALL applications 
(particularly in the primary school scenario) is to 
exploit underlying technology to present 
information in a manner appropriate to the learner. 
To this end we developed animation software 
interfaced with the output generated by the FST 
engine.  
Animation can enhance the learning process and 
is especially interesting for younger learners. 
Flash (2004) is a useful software environment to 
develop animations but it is difficult for non-
programmers to use and it is often difficult to use 
the same animation templates for different inputs.  
One solution is to use XML (Extensible Markup 
Language, XML (2004)) files as input into Flash, 
so that the information displayed is customisable 
according to the information in the input data file. 
We outline how animated CALL materials were 
developed for teaching the conjugation of verbs in 
the present tense in Irish. 
Output from the FST engine is fed to a Perl 
script which converts the information into a 
specified XML format.  The XML files are then 
used by Flash to generate the required animation.  
Figure 1 outlines the software architecture. Figure 
2 shows the conjugation of the verb cuir (to put) in 
the present tense in Irish. Figure 3 shows modified 
output from the FST engine to enable automatic 
animations to be generated (^INF indicates 
inflectional infix, ^PP indicates inflectional 
postposition and ^SUF indicates inflectional suffix  
for Flash).  
 
 
FST 
Output
XML
Files
Perl 
 
 
 
Flash Animation 
 
Figure 1: Software architecture 
 
1S Chuir mé 
2S Chuir tú 
3S Chuir sé/sí 
1P Chuireamar 
2P Chuir sibh 
3P Chuir siad 
 
 
 
 
 
 
Figure 2: Conjugation of "cuir" 
 
 
 
A section of the XML file that feeds into the
Flash program
 
 
 
 
 
 
 
 
 
 
 
The ani
"cuir" is split
"h" is inserted between "c" and "uir". Finall
postposition "mé" is added (Figure 5).   
Anim
any
FST engine, as all 
PastInd  c^INFuir^PP  
PastInd+1P+Pl  c^INFuir^SUF 
Figure 3: Sample output from FST engine 
 
 is shown in Figure 4. 
<verb>cuir</verb> 
<stem1>c</stem1> 
<stem2>uir</stem2> 
<infix>h</infix> 
<fir_sg><postpos>mé</postpos></fir_sg> 
<sec_sg c_sg> 
<thi_sg><postpos>sé/sí</postpos></thi_sg>
<fir_pl><suffix>eamar</suffix></fir_pl> 
<sec_pl><postpos>sibh</postpos></sec_pl>
<thi_pl><postpos>siad</postpos></thi_pl> 
Figure 4: XM
ations can be deve
 verb and m
L file for Flash program
mation movie demonstrates that
 up into "c" and "uir". T
loped automati
orphological process known to the
morphological operations are 
><postpos>tú</postpos></se
 
 the stem 
hen the infix 
y the 
cally for 
 
coded for Flash.  This removes the necessity of 
hand-coding animations and reduces the risk of 
human error. 
 
 
 
 
 
 
 
 
 
Figure 5: Snapshot sequence from 
animation movie for past tense 1
st
 person 
singular for the verb ‘cuir’ in Irish  
(Inné means yesterday) 
 
The Flash-based interface dynamically displays 
XML data. It reads in XML data at runtime and 
generates an animation. Learners have full control 
over the animation. They can play, stop, rewind 
and skip through the animation. Further interaction 
is provided via menus to choose specific 
conjugations (e.g. number, person and tense.) 
The FST-Flash interface is language-
independent. The XML files contain detailed 
information about the different string operations 
and the corresponding targets. The only operations 
known to the Flash interface are insert, delete and 
replace. In this way, the animation of language 
data is abstracted from linguistic terms like 
prefixation, suffixation or lenition, thus avoiding 
the problem of varying definitions of these terms in 
different languages. The transformation of the 
(linguistically tagged) output from the morphology 
engine to the XML data necessary for animated 
presentation is done by Perl scripts which can be 
tailored specifically to each combination of 
language and output of a NLP tool. 
5 CL/NLP Resources for German Primary 
School CALL  
5.1 Background 
German is gradually being integrated into Irish 
primary schools through the Modern Languages in 
Primary School Initiative (MLPSI), which has 
been running since 1998. At present, over 300 
schools in Ireland are involved in the MLPSI.  
German is taught during the senior two years of 
the primary school cycle (children aged 10-13). 
Irish students do not receive any instruction in 
Modern Foreign Languages (MFL) up until this 
point (Irish is not considered a MFL). The 
communicative curriculum we developed is based 
on a draft curriculum which was developed by the 
National Council for Curriculum and Assessment 
(NCCA) (NCCA, 2004) for teachers participating 
in the MLPSI. 
The integration of type A CL technology into 
CALL in this environment is ideal. The target 
language is restricted to a beginner’s curriculum. 
This represents a restricted domain. Sentence 
constructions are simple with few structures that 
could present coverage or ambiguity difficulties to 
CL systems. Given that the target language is 
German, many CL tools are available for almost 
every aspect of language processing. 
In this section we will focus on the use of type B 
NLP technology in this environment to meet the 
needs of students learning German. These needs 
have been researched qualitatively through 
observation during German language lessons in a 
primary school in Ireland during the school year 
2003/4. Irish students are native English speakers 
(some are also native Irish speakers) and as such 
are unfamiliar with nouns being associated with 
genders as in German. These students also require 
extra practise with inflecting verbs correctly. 
Having being asked ‘Wie heißt du?’, students will 
often respond with ‘Ich heißt …’, for example. We 
present the use of a POS tagger in the development 
of a tailored corpus which subsequently feeds into 
the automatic generation of exercises.    
5.2 Technology – POS tagging, Perl and XML 
CALL courseware generally presents users with 
exercises to complete after they have studied a 
particular topic. These are usually static in content 
and are very time consuming to develop over the 
full set of language topics. Students are usually 
presented with a small number of exercises, which 
they will have completed in their entirety and 
become familiar with in a limited space of time. 
Larger sets of exercises prove beneficial in 
providing variety for the student – they will not be 
presented with the same set of exercises each time 
they visit a topic. In addition, some students will 
complete exercises faster than others. This puts 
pressure on slower students to keep up and on 
teachers to provide alternative work to keep faster 
students occupied. Larger sets of exercises mean 
that exercise selection can be randomised so that 
students are p esented with new material each time 
they visit t
less pre
students complete additional exercises within the 
same language topic and teachers 
required to pr
CL can significantly
generate set
 
A co
the NCCA guidelines and tagged 
Sch
The annotated text file 
converted to
divided into 
topic. Addi
file referen
file at this stage.  
 
 
 
 
 
 
 
 
 
Once the annotated corpus
XML it can
such as lesson generation, automatic dictionar
extraction, a concordancer and automatic 
generation of
on t
verbs, articles and nou
identifies. Inflection and article-noun com
can be practised when a student chooses the correct 
verb ending or article from a selection or types in 
the correct answer. A version of hangman (a game 
where students try to guess an unknown word by 
guessing letters in the word - they only get a 
certain number of chances for incorrect answers 
after which the game ends) can also be played with 
article-noun combinations. By simply specifying 
the topic section in the curriculum and the type of 
game, exercises are automatically generated. Each 
particular exercise is randomised so that the user is 
presented with a new variant of the problem each 
time they attempt an exercise or game. 
 
Multiple-choice
Exercises
 
 
 
 
 
 
Gap-fill 
Exercises
Annotated 
Corpus in XML
Curriculu
r
he courseware; slower students will feel 
ssure to work at a faster pace when faster 
will not be 
ovide alternative material.  
 reduce the time needed to 
s of exercises around language topics. 
mplete curriculum was developed around 
using Helmut 
mid’s TreeTagger (see TreeTagger homepage). 
was then automatically 
 XML using Perl. The corpus is 
separate XML files for each language 
tional information - audio and graphic 
ces were added manually to each topic 
 
 
 
Figure 6: Generating annotated corpus in XML 
 has been converted to 
 feed into a number of applications 
y 
 various exercise types. In focusing 
he latter, we are particularly interested in the 
ns that the POS tagging 
binations 
 
 
 
Figure 7: Automatic Exercise Generation 
 
Previous work in automatic exercise generation 
from corpora highlighted a number of potential 
pitfalls (Wilson, 1997). Most importantly, the 
language in the corpus used is best when the 
linguistic quality of the texts is appropriate for 
learning a language. Long and complex sentences 
are best avoided. Our design employs a corpus 
collated and tailored around the learner’s 
curriculum, thus avoiding this pitfall.  
The benefit of using CL resources here is similar 
to the situation in the Irish context. Exercises can 
be developed automatically for any verb or noun 
phrase within the curriculum and provide variety 
for the user. This removes the nece hand-
coding each exercise and reduces the risk an 
error. 
POS 
Tagger 
6 Conclusion 
It is difficult to integrate CL/NLP resources into 
CALL, especially as these resource
generally designed with a CALL audience in 
However, there are environments wher
be successfully integrated, especi
imaginative and useful way.  The technolog
not have to be particularly
complex - what is important is that it is 
appropriately deployed.   
This paper outlined how two NLP resources can
be used in the development of CALL resources for
primary schools.  It is novel in the ICA
employ CL/NLP technologies for 
especially when they are beginners in learning a 
Complete 
m 
Perl 
Perl 
Annotated 
Corpus in XML
(individual 
language topics)
Hangman Game 
Additional info. – 
graphics, audio files 
ally
 rev
ssity of 
 of hum
s are not 
mind.  
e they can 
 if used in an 
y does 
olutionary or 
 
 
LL world to 
young learners, 
language. We outlined how the output of a FST 
engine can feed into the generation of Flash 
animations for Irish verb conjugations. We showed 
how a POS tagger can be used to annotate a 
curriculum to produce a corpus which can in turn 
be used to automatically generate exercises. Both 
of these initial modules will be comprehensively 
deployed and evaluated in the classroom during the 
coming school year (Sept. 2004-June 2005). Future 
modules will include type A CL technology like 
DCGs and will take advantage of the controlled 
languages and limited domains which exist in the 
primary school environment. Each module of the 
overall system is being developed so that 
concurrent evaluation can be carried out.  
This paper highlighted the point that even 
though neither of these NLP resources was 
developed with CALL applications in mind, when 
combined with relatively straightforward 
programming and interface techniques, they can be 
used fruitfully in a CALL environment. 
7 Acknowledgements 
This research has been funded by SFI Basic 
Research Grant SC/02/298 and IRCSET Embark 
Initiative Grant RS/2002/441-2. 

References  
D. Arnold, L. Balkan, S. Meijer, R. L. Humphreys 
and L. Sadler. 1994. Machine Translation - An 
Introductory Guide NCC Blackwell Ltd., 
London, USA. 
K. R. Beesley and L. Karttunen. 2003. Finite-State 
Morphology. Series: (CSLI-SCL) Center for the 
Study of Language and Information. 
H. D. Brown. 1994. Principles of Language 
Learning and Teaching. Prentice-Hall Inc, 
London, Sydney, Toronto, Mexico, New Delhi. 
D. Dokter and J. Nerbonne. 1998. A Session with 
Glosser-Rug. In “Language Teaching and 
Language Technology” S. Jager, J. Nerbonne, 
and A. van Essen, ed., pages 88-94, Swets & 
Zeitlinger, Lisse. 
Flash. 2004.  Available at: 
http://www.macromedia.com/software/flash/ 
 [Accessed 10 April 2004] 
FreeText. 2001.  FreeText Homepage.  Available 
at: http://www.latl.unige.ch/freetext/ [Accessed 
10 April 2004] 
D. Hetherington. 2000.  Computer Resources for 
the Teaching of Irish at Primary and Secondary 
Levels. Language Centre NUI Maynooth, 
Ireland. 
J. Harris and L. Murtagh. 1999. Teaching and 
Learning Irish in Primary School. ITE, Dublin. 
W. J. Hutchins and H. L. Somers. 1992. An 
Introduction to Machine Translation. Academic 
Press, London. 
R. Khader, T. Holloway King and M. Butt. 2004. 
Deep CALL grammars: The LFG-OT 
experiment. DGfS 26.Jahrestagung, Mainz, 
Germany. 
T. Koller. 2004. Creating user-friendly, highly 
adaptable and flexible language learning 
environments via Flash, XML, Perl and PHP. 
Presentation at the EuroCALL SIG-LP workshop 
"Innovative Technologies and Their Didactic 
Application", Vienna, September 2004. 
M. D. La Torre. 1999. A web-based resource to 
improve translation skills. ReCALL, 11(3): 41-
49. 
W. Menzel, D. Herron, R. Morton, D. Pezzotta, P. 
Bonaventura, and P. Howarth. 2001. Interactive 
pronunciation training. ReCALL, 13(1): 67-78. 
NCCA. 2004. National Council for Curriculum   
   Assessment (NCCA) Homepage. Available at:    
   http://www.ncca.ie/j/index2.php?name=currinfo   
   [Accessed: 10 April 2004] 
V. Reuer. 2003. Error Recognition and Feedback 
with Lexical Functional Grammar. CALICO, 
20(3): 497-512 
M. Schulze. 2003. AI in CALL: Artifically Inated 
or Almost Imminent? WorldCALL 2003, Banff, 
Canada. 
TreeTagger Homepage. Available at: 
http://www.ims.uni-stuttgart.de/projekte/corplex/ 
TreeTagger/DecisionTreeTagger.html 
[Accessed: 20 April 2004] 
E. Uí Dhonnchadha. 2002. An Analyser and 
Generator for Irish Inflectional 
Morphology Using Finite-State Transducers. 
Msc Thesis. 
A. Vandeventer Faltin. 2003. Natural language 
processing tools for computer assisted language 
learning. Linguistik Online 17, 5/03 
E. Wilson. 1997. The Automatic Generation of 
CALL Exercises from General Corpora. In 
“Teaching and Language Corpora” A. 
Wichmann, S. Fligelstone, T. McEnery, and G. 
Knowles, ed., pages 116-130, Addison Wesley 
Longman, London. 
XML. 2004.  Extensible Markup Language.  
Available at: http://www.w3.org/XML 
[Accessed 10 April 2004] 
