Semantic Context Visualization to Promote Vocabulary Learning

Traditional access through the alphabetically organized
macrostructure (words defined) of the dictionary was convenient
in a printed form. Online versions of dictionaries lead us to
rethink our access approach and to exploit, as suggested by
Humblé, the increased value of the dictionary in computer
assisted language instruction. In a self-learning environment,
a situation favored by today’s wide-spread access to computers,
an online dictionary is a valuable resource for reading
comprehension. However, learners can quickly become
discouraged if the information about a word searched for is
buried among too much other information, e.g. the many
definitions listed for highly polysemous words. Our current
research suggests a method for providing specific guidance to
a user to ease his access to information and promote vocabulary
learning during his dictionary searches.
We present a tool, REFLEX, built on a mathematical model of
a fuzzy logic search engine. We suggest that the microstructure
(the content of the entries) of a dictionary be considered as a
corpus. From this corpus, using fuzzy operators, we can
calculate a similarity matrix, called a fuzzy pseudo-thesaurus
(Miyamoto), expressing the degree of association between each
pair of lemma (base forms of words) found in the corpus. This
similarity calculation is based on the tendency of two words to
co-occur within sentences. The fuzzy pseudo-thesaurus is
pre-calculated and used at the search (query) time (Klir and
Yuan).
Presently, the tool is for English learners of French and uses a
learner`s dictionary called DAFLES (Dictionnaire de
l’Apprenant du Français Langue Seconde et Étrangère)
(Verlinde et al.) merging all its defining sentences to build a
corpus. Portability to languages other than French would require
work at the pre-processing stage, for example to tokenize the
sentences (split them into word units) and lemmatize the words
(e.g. lemma should be found for diverse forms of nouns and
verbs). Portability to other types of dictionaries would require an understanding of the different types of information
(definitions, examples, notes) contained before merging them
into a corpus. Each type of information might be of different
value to a learner, and then could be given more or less weight
in our model.
REFLEX provides a graphical visualization of a word’s semantic
context. Figure 1 shows the portion of the pseudo-thesaurus
relating to the word “scientifique” (scientific) as automatically
built from DAFLES. The surrounding words (chercheur –
researcher, étudier – study, observation – observation, théorique
– theoretical, rechercher – research, etc.), express words related
to scientifique, their graphical distance being proportional to
their calculated distance.
Figure 1: Semantic context for scientifique
Figure 2: Semantic context for météorologique (meteorological)
Semantic contexts give a graphical representation similar to
semantic maps which are widely used in language teaching to
favor vocabulary acquisition (Brown and Perry; Carrell; Crow
and Quigley). Semantic maps are usually provided in classroom
settings and are manually constructed by instructors
interactively with the students. Here semantic contexts are
produced in real-time, and REFLEX provides navigation
capabilities for the user to easily move to other portions of the
pseudo-thesaurus. For example, a click of a mouse on the word
météorologique within the semantic context of Figure 1 brings
the learner to the semantic context shown in Figure 2.
The implicit relations shown through the arcs linking the
concepts are not limited to paradigmatic relations as found in
WordNet (Fellbaum) or MindNet (Richardson et al.)1 For
example, the association between observation and researcher
in Figure 1 could not be labeled with a paradigmatic relation.
The complex relation, typical activity, is closer to a lexical
function as defined by Mel’cuk.
REFLEX also serves as a guide for dictionary searches by
making use of information found in the context of occurrence
of the unknown word. It leads the learner not necessarily to the
entry corresponding to that unknown word, but to any
information from dictionary entries likely to help the
understanding of the word within that particular context. This
provides a clear filter for polysemous words and helps the
learner identify among all the possible definitions and examples,
the ones most relevant to their current reading situation.
Let us take for example the French polysemous word culture,
which could relate to cultivate (flowers or vegetables) or culture
(social knowledge). The following sentence is taken from a
text "L’école dans les deux langues" shown in a reading
comprehension software (DidaLect, Duquette et al.): "En
étudiant la langue et la culture de l’autre, Anglo-Saxons et
Latinos apprennent en même temps à se connaître et à se
respecter." .
Assuming the word culture is unknown to a learner, she
launches a search. The context window of occurrence of that
word is analyzed to obtain a query vector made up of the
searched word and its neighbours (culture, étudier, langue,
apprendre, connaître, respecter)2. This query vector is
expanded on via the fuzzy pseudo-thesaurus to include related
words, each with a weight corresponding to their similarity to
the original query vector (culture 1.0, étudier 1.0, langue 1.0,
apprendre 1.0, connaître 1.0, respecter 1.0, didactique 0.1,
pédagogique 0.09, éducatif 0.09, créatif 0.08, race 0.08, impoli
0.06, curiosité 0.06, brassage 0.06, fermier 0.06, formateur
0.06)3. The enlarged vector then represents an extended context
of a word, and is used in an information retrieval task to extract
interesting definitions and examples pertaining to that context
from the corpus of sentences. These sentences are presented to
the user in decreasing order of relevance, as shown in Table 1
(relevance is given in first column), and come from various
polysemous entries. Table 1 – Relevant sentences for the understanding of the word culture in
context
Using the extended context of a word, combined with
knowledge of word association as pre-calculated in the
pseudo-thesaurus, REFLEX gathers the distributed information
from the dictionary to help the understanding of a word (often
polysemous) as expressed in a specific reading context.
Yet, free browsing is not necessarily a bad thing for learners.
In fact, researchers in second language learning (Aston) debate
on the usefulness of corpora in which learners could be left to
wander among sentences by themselves. On one hand, free
browsing mode encourages autonomous discovery of different
or new senses of words, but on the other hand, too much
discovery within a reading comprehension task might take the
learner too far from the original goal. Furthermore, both
activities (free or guided exploration) are more or less
appropriate depending on the learner`s profile (efficient or less
efficient learner).
Although intended to guide, REFLEX is easily adaptable toward
a discovery mode by simply removing the contextual cues from
the query vector. The flexibility of REFLEX can also be seen
in the presentation of the semantic maps. For efficient learners,
larger maps can be shown, such as presented in Figure 1, but
for less efficient learners, maps can be restricted to only contain
the few closest words.
In conclusion, REFLEX shows much potential for online
dictionary exploration. It is constructed on sound mathematical
principles and has potential for adaptability to different learning
purposes and types of learners. As our next step, we will
integrate REFLEX as a module within DidaLect, a reading
comprehension software, with focus on adaptability parameters.
Investigation into automatic labeling of associations is an
ambitious longer term research goal. Finally, we hope for future
explorations with other dictionaries and other languages.
1. Paradigmatic relations, such as hyperonymy, meronymy,
synonymy, are of great value in language learning and do provide
an explicit way for learners to organize the new vocabulary in
their own knowledge stores. The purpose of semantic contexts is
complementary to this organization by providing a semantic field
around a word.
2. Lemmatized forms of content common nouns are taken. We do
not use proper nouns (they are not in the dictionary) or function
words.
3. We only show values larger than 0.06, but this threshold could be
adjusted.
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