2023 8th International Conference on Business and Industrial Research(ICBIR2023)

18 - 19 May 2023

Consideration of Language Learning Service with
Visualized Vocabulary Map Derived from WordNet
Masatsugu Ono
Faculty of Engineering
Muroran Institute of Technology
Muroran, Japan
onomasa@mmm.muroran-it.ac.jp

Toshioki Soga
Faculty of Science and Technology
Chitose Institute of Science and Technology
Chitose, Japant-soga@photon.chitose.ac.jp

Masato Kikuchi
Faculty of Engineering
Nagoya Institute of Technology
Nagoya, Japan
kikuchi.masato@nitech.ac.jp

Tetsu Tanabe
Information Initiative Center
Hokkaido University
Sapporo, Japan
ttanabe@iic.hokudai.ac.jp
Abstract— The authors aim to propose highly effective,
efficient and satisfactory service of language teaching andlearning by means of educational technology from the viewpointofusability
engineering.
In
the
field
of
computational/quantitative linguistics based on corpora data,
many linguists have been numerically analyzing lexicalcharacteristics for each English vocabulary and compiled themas a vocabulary list which is machine-readable since 1960s. On
the other hand, a lexical database of semantic relations called
WordNet has also been developed in the field of computing
science. This study consists of three components. One is toenrich, refine and integrate already-existing vocabulary
information in terms of “frequency”, “difficulty”, and“relativeness”. Another is to develop the system of language
learning and teaching service with the integrated vocabulary
information. And the other is to evaluate the system fromusability engineering point of view. So far, the authors reachedand conducted the first two kinds of studies, succeeded inderiving the newly-integrated vocabulary list and visualizing theword relations in each experiment.
Keywords—vocabulary lists, visualization, conceptual
structure map, language learning service, user experience

I. INTRODUCTION
The purpose of this study is to improve present activities
and situations of English language education for both learners
and teachers. And the goal is to suggest taking a new strategy
of vocabulary learning from the viewpoint of service science.
The authors aim to propose highly effective, efficient andsatisfactory service of English vocabulary teaching andlearning by means of educational technology. They regard a
series of actions on language education as a “service” as well
as regard both learners and teachers as “users” in the field ofusability engineering. They will evaluate the educational
contents and services that they developed, based on user
experience research.
In this study, they attempted to generate vocabulary maps
in order to show English learners difficulty, frequency andcorrelation of each word. This paper describes and focuses on
how to visualize such information of lexical data of English
vocabulary.

II. BACKGROUND
A. English Language Education in Japan
A generation ago, students in Japan started learning
English when they became junior high school students. But
present students in Japan start learning it when they get to the
3rd grade in elementary school after the year 2020. They learn
English as a first foreign language, not English as a second
language. It means that they do not have any opportunities toencounter or be involved in real English situations in their
daily life in Japan and also means that the period of theirlearning English at school has just been extended. Normallythey have an English instruction from teachers with textbooks
in English learning classes. They learn English vocabulary as“new words” in each lesson from textbook, which often makes
them difficult to learn through their self-study. For Japanesepeople, their first language is Japanese which is quite different
from English in grammar like word orders, pronunciation, etc.
It is still very difficult for most Japanese people to master
English due to such language difference.
B. Characteristics and Education of Vocabulary
According to Tamamura[1], vocabulary has its own
characteristics. Some of the typical ones are shown as below.
• Many words can be reworded or rephrased.
• The number of words keeps on generating andincreasing more.
• The rate and range of one word usage differ fromanother.
• A sense of language also differs between different
languages.
Tamamura also describes that instructors should befamiliar with the word set to be taught at the same time as wellbecause words can newly generate and increase as one of thecharacteristics. It means that words to be taught should becollected and/or classified as synonyms, co-occurred words,
derived words, etc.
Sonoda[2] produced a lexical dataset called Hokkaido
University English Vocabulary List (HUEVL), in which 7453
words are selected as learning-purpose vocabulary at school.

2023 8th International Conference on Business and Industrial Research(ICBIR2023)
In the list, each word is classified into 5 groups from level 1(beginner: junior high level) to level 5 (advanced: university
level). Sonoda selected those words as frequently-used as wellas basic vocabulary and described that the list would be a kindof end-point (or destination) of vocabulary learning travel forlearners and also warrant the instruction of vocabulary
learning.
Aoyama[3] describes that the academic society of English
education called COCET compiled a vocabulary list which
included 3300 words for technical college students to learn
during the period of their 5-year school days. A concept of thelist is to include potentially required words for engineering use
at work after their graduation. Another is to learn them withmultimedia devices. The list was not only published as a wordbook but also developed as a smartphone app as shown in
Fig.1.

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• Teaching as well as learning time is still limited in theclassroom at school.
On the other hand, there are also problems to solve in thefield of language learning service, which would make degreeof user satisfaction lower.
• There are vocabulary learning systems using
smartphone apps as well as websites. Students canlearn freely if they have a smartphone or a tablet withthem. But most of the systems are based on a one-toone correspondence between Japanese and English. It
might make flexibility or applicability low.
• Such systems are mainly based on textual information.
There is little vocabulary information that visually orgraphically makes users understand the applicability ofwords, such as conceptual diagrams.
B. Research Hypotheses
Based on the present problems in the former section, thenthe authors set up the following research hypotheses.
• If an appropriate service of language learning support
that explicitly shows co-occurrence relationships andconceptual connections between words is provided forlearners as a user, then it will become possible for themto learn new vocabulary with a greater awareness ofthe connections. And they will be able to reduce lessamount of their mental burden in memorizing each
new word.

Fig. 1.

Examples of word book (L) and smartphone app (R)C. Effects and Problems of Vocabulary Lists
Such books and apps as in Fig.1 are considered as goodeducational materials at a glance that make learning effective
and efficient. However, there are some researchers who
pointed out problems on vocabulary learning with them.
Kawamura et al.[4] pointed out that students still ran short oflearning time to familiarize themselves with new words evenin elementary school. He cited students’ quick-fix or temporalapproach to vocabulary learning as an example due to tightlearning schedule at school.
Kaneta[5] pointed out that already-existing vocabulary
lists were apt to be selected subjectively and have both goodand bad aspects. He said that the former was the lists increased
educational validity and that the latter was subjective lists
whose name is like “basic words for daily conversation” or“necessary words for English instruction” left a problem ofobjectivity in processing data.
III. RESEARCH QUESTIONS AND HYPOTHESES
A. Problems to Solve in Vocabulary Education
There are several problems to solve in the field of
vocabulary education. These are considered as making theactivities of vocabulary learning ineffective and inefficient.
• Although quantity as an end point (or a destination) for
learning words is indicated as a vocabulary list, it is not
explicitly delivered to learners.
• Even if there is an end point, the specific order andprocedure to learn words is still unclear. It is as if
students would have to learn just like groping in thedark.

• If the appropriate service through vocabulary grouping
with visualized vocabulary maps is provided as alearning service, then learners as a user will be able toobtain higher motivation and satisfaction of language
learning by contacting multiple words at once.
The service will increase the efficiency of learning workand make it easier to obtain the effect of long-term memory
retention compared to before. Rather than learning words
individually, it will be better to learn words that are connectedby grouping together because the learners will find moreroutes to access words. So it can be easier to remember
memories, reduce the burden of memory, and reinforce theefficiency of learning work.
IV. DEVELOPMENT OF CONTENTS
A. Vocabulary Size
Lonsdale[6] shows an interesting statistical data forcommunicative language learning. He shows that 85% wordsof daily conversation are covered if a person acquires first top
1000 words in frequency and that 98% words are covered if
he/she acquires first top 3000 words in frequency. It is showngraphically in Fig.2. The authors regard these number 1000
and 3000 as an important key for language education services.
This research is an exploratory research. In order tofacilitate this vocabulary analyses, the authors started byfocusing on as few core vocabulary words as possible. As
mentioned in the previous section, with elementary school
English in mind, the lexical frequency information to behandled is limited to nouns and verbs that are the mainelements of English sentences. 2,105 words that are within the
top 3,000 ranks in Table 1 are targeted, according to the
vocabulary size of Lonsdale’s scales.

2023 8th International Conference on Business and Industrial Research(ICBIR2023)

18 - 19 May 2023

In terms of English vocabulary in start-up level learning,nouns have more than verbs within the top 1000 frequencies.
The ratio is about 1:1.43. In addition, when the frequency is
expanded to the ranking down to the top 3000, the differencein the number of nouns and verbs in terms of lexical
occurrences widens further. The ratio becomes 1:1.78. It was
found that the verbs increased by about 310 words from thejunior high school level to the university entrance exam level,
while the nouns increased by 874 words. The need tomemorize more nouns than verbs may be reflected in theincrease in the amount of individual human knowledge.

Fig. 2.
TABLE I.

Graph of Word Frequency and Counts

NUMBER OF WORDS IN FREQUENCY RANK IN BNC LIST

B. Integration of Vocabulary Lists
As for the datasets of frequency, many researchers have
tried to compile various vocabulary lists for language analyses.
Kucera and Francis[7] compiled a dataset based on a text data
which consisted of a million words called Brown Corpus. Thisis the first computational analysis of English vocabulary in theworld in 1960s. Kilgarriff[8] compiled another dataset basedon a hundred million word corpus called British National
Corpus. It includes 6318 different words each of which occurs
over 800 tokens in frequency with its part-of-speech tags. In
Japan, Sonoda compiled a vocabulary list for English learners
from language education point of view, as already describedabove.
TABLE IV.

VOCABULARY LISTS FOR INTEGRATIONOn the other hand, in narrowing down the learning
vocabulary, the authors focused on the top 1000 and 3000
words, which are Lonsdale's scales, and analyzed 4659 words
from levels 1 to 3 of HUEVL, shown in Table 2.
TABLE II.

NUMBER OF WORDS IN DIFFICULTY BASED ON HUEVLIn the BNC vocabulary list, the authors extracted therelationships between hypernyms and hyponyms in WordNet,targeted at only nouns and verbs in the top 3000 with highfrequency of use as well as in the difficulty vocabulary list of
HUEVL, both of which are regarded as highly important incommon. Table 3 shows the comparison of vocabularyfrequency and occurrence distribution of learning vocabulary.
TABLE III.

Kikuchi and Ono[9] attempted to integrate the following
vocabulary datasets of Table.4. They conducted text
processing through Perl programming with a linux computerand succeeded in compiling the integrated datasets of English
vocabulary lists which includes 15885 types of word in total,
as shown in Fig.3.

DISTRIBUTION OF FREQUENCY AND DIFFICULTY

Fig. 3.

Example of integrated vocabulary lists

Each vocabulary list has its own characteristics. COCET
3300 was edited as a science-oriented list. Two Sugiura lists
were based on the vocabulary which appeared in the highschool textbooks of English subject approved by the Japanese
Ministry of Education. They show vocabulary in educational
purposes. The words in SVL 12000 were selected for every
level of Japanese learner of English from primary school to2023 8th International Conference on Business and Industrial Research(ICBIR2023)
postgraduate level in terms of “usefulness” and “importance”
as well as “frequency”, according to ALC Press[10].
C. Extraction of WordNet Vocabulary Data
Vocabulary datasets are derived from the field ofcomputing science as well as linguistics. One of the mostsignificant computing datasets is WordNet, which was firstly
developed by Fellbaum[11]. It consists of about 150,000
words of nouns, verbs, adjectives and adverbs, and also
includes about 210,000 patterns of each word spelling anddefinition. Now everyone can download the database for freefrom the designated website of Princeton University.
According to the website, these words in WordNet aregrouped into sets of cognitive synonyms called synsets. And
the synsets are interlinked by means of conceptual-semantic
and lexical relations.

Fig. 4.

TABLE V.

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ACCENT COLORING FOR ANALYZED WORDS

To extract and visualize the vocabulary data as a
vocabulary map from WordNet, the list of JACET in addition
to BNC and HUEVL was used in order to highlight importantwords. First, 2105 words were selected as shown in Table1,
which are ranked in the top 3000 in BNC list. Then, 1497
words were extracted out of the 2105 words that occur incommon from level 1 to 3 corresponding to junior high levelto university exam level in both JACET and HUEVL lists.
Finally, the last 1497 words of conceptual structures between
each word were extracted and visualized from WordNet aswell as color-highlighted by level as shown in Table 5. andFig. 5.

Flow of extracting conceptual structures from WordNet

D. Visualization of Vocabulary Map
When language learners try to study their target language,
they tend to focus on not only a single word itself but also aseries of words like “chunks” and “collocations” as well as
grammatical and idiomatic phrases. They also tend to demandparaphrastic or interchangeable expressions so that they can
substitute an alternative word or phrase for what they want tosay first. WordNet will be a possible solution because it hassynonyms as “synsets”, as it were, a thesaurus. It also has
another information of hypernyms or hyponyms as each word
relationship. Such information will help the learners with theirforeign/second language acquisition.
The research group including the authors[14] attempted toutilize WordNet to enrich the database with hypernymhyponym relation of each vocabulary. In order for language
learners to understand the word relation, the researchers tried
to visualize the hierarchical word relation, as shown in Fig.5.
Based on electrical dataset in WordNet, they firstly set up atentative word search program of WordNet under linux
environment through Python programming and then extracted
visualized images for each word derived from the integrated
vocabulary database.

Fig. 5.

Conceptual Map: Visualization of Word Relationship

V. CONCLUSION AND FUTURE STUDIESThis paper showed how to select important words forvocabulary learners, and how to extract and visualize
vocabulary map with them from WordNet from the viewpointof contents development. The derived data and map of words
relationship will be used for future language learning service
in the next step. Service system needs to be developed and also
evaluation model for effectiveness, efficiency, andsatisfactory will have to be examined as future studies.
ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant
Number JP22K02825.
The authors are grateful to Mr. Haruka Hosokawa and Mr.
Mikiyasu Nishiyama for collaboration on the early stages ofthis work.

2023 8th International Conference on Business and Industrial Research(ICBIR2023)
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