258
Chapter 17
Combinatory Categorial
Grammar for ComputerAssisted Language Learning
Simon Delamarre
Telecom Bretagne, France
Maryvonne Abraham
Telecom Bretagne, France & Université Européenne de Bretagne, France & Laboratoire LaLICC,
Paris Sorbonne, France
ABSTRACT
This chapter intends to demonstrate how Applicative and Combinatory Categorial Grammar (ACCG)
can be drawn on to design powerful software applications for the teaching of languages. To this end, the
authors present some modules from their “pictographic translator,” software that performs syntactical
analysis of sentences in natural language directly written by the user, and then dynamically displays
series of pictograms that illustrate the words and structure of the user’s sentences. After a short presentation of the application and an introduction to ACCG, the chapter examines how this formalism enables
the building of several high-level functions in the system, such as disambiguation, structure exhibition,
and grammatical correction/validation. The chapter concludes with a short discussion concerning the
potential (and limits) of this architecture with regards to multilingualism.
INTRODUCTION
According to certain studies concerning learning
and retention, we usually memorize about 90%
of what we do, versus only 10% of what we read.
And indeed, however forced this explicit quanDOI: 10.4018/978-1-61350-447-5.ch017
tification may seem, it is a fact that any learning
activity, to be efficient, has to be active. It is in
this spirit that, while the huge majority of current
pedagogical software programs mainly rely on
cross-the-correct-answer/fill-the-gap exercises,
we conceived this interactive pictographic translator. It consists of a graphic interface, in which
the user is invited to build a sentence, by typing
Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Combinatory Categorial Grammar for Computer-Assisted Language Learning
his own words. When the programs detects that
a new word has been entered (using a tokenizer),
it computes a syntactical analysis of the currently
built sentence, and then, retrieves (by performing
a lemmatization) and displays a pictogram that
represents this word (see Figure 1). As often as
it remains relevant, we use the rule of correspondence: one word, one pictogram. Situations in
which an only pictogram for several words would
be preferable (e.g. washing machine) are treated
in a post-processing of the tokenisation step, by
merging tokens that form a wider component
present in the lexicon ((washing,machine) →
(washing machine)).
At the same time, by using several graphical
effects, such as colored borders, subtitles, transparencies, small pictograms to indicate functions
(plural, tenses...), the program gives to the user
direct visual control of the sentence he is writing,
enabling him to detect problems if there are any
(see figure 2). If however the user is not able to
correct one or several mistakes by himself, he
may ask the program to indicate these for him.
Thus, little by little, through this process of writing/visual control/correction, the learner is led to
build by himself correct sentences, without needing any other exterior help, which will bring him,
beside the satisfaction of autonomy, a better understanding of the structures and mechanisms of
language. Furthermore, in the case of pupils learning reading, this ludic activity of “making pictures
to appear” may conduce them to grasp the expressive power of words.
This chapter aims to present the main mechanisms and ideas our pictographic translator is
based on. After giving to the reader some elemental knowledge about the formalism used (ACCG)
and the theoretical issues involved, we will pres-
Figure 1. The pictographic representation for “Je souris à la petite souris” (I’m smiling at the little
mouse). The pictograms appear dynamically as the words are entered.
259
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