International Journal of Management and Applied Science, ISSN: 2394-7926

Volume-2, Issue-5, May.-2016

AN IMAGE RECOMMENDER SYSTEM THAT SUGGESTSAPPROPRIATE IMAGES IN CREATION OF SELF-LEARNING ITEMSFOR ABSTRACT NOUNS
1

MOHAMMAD NEHAL HASNINEA, 2YUKI HIRAI, 3MASATOSHI ISHIKAWA,
4HARUKO MIYAKODA, 5KEIICHI KANEKO, 6LYN PEMBERTON

Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan
Faculty of Business Administration, Tokyo Seitoku University, Tokyo, Japan
Department of English, Tsuda College, Tokyo, Japan
School of Computing, Engineering and Mathematics, University of Brighton, Brighton, UK
E-mail: 50015834303@st.tuat.ac.jp, yhirai@cc.tuat.ac.jp, ishikawa@tsu.ac.jp, miyakoda@tsuda.ac.jp,
k1kaneko@cc.tuat.ac.jp, Lyn.Pemberton@brighton.ac.uk

Abstract- This article acquaints its readers with a research paradigm that has been designed to extract appropriate(synonymously educational) images of abstract nouns. The ongoing investigation principally purposed to assist L2
learners/educators by recommending appropriate images in the creation of vocabulary learning items for abstract nouns.
Hence, a prototype version of an image recommender system that purposed to assist the users in the recommendation of
appropriate images for 3-types of abstract nouns has been implemented and tested. The proposed system, in the process oflearning items creation, allows the users to select their own preferred image if the system recommended topmost image is notsatisfactory. Our study presumed that still images having physical or concrete existence can be addressed as appropriate
learning resources in the representation of abstract nouns that firstly, represent social contexts between human, secondly,
related to feeling and emotion, and thirdly, state social or religious belief. Authors agree that due to huge cultural influences
and multiple behaviors of abstract nouns, this hypothesis may be a matter of debate. Therefore, to assess the images, an image
evaluation experiment has been conducted with 20 participants who are actively engaged in foreign language acquisition. A
post-hoc analysis of Tukey’s test revealed the significant difference (P=0.04) of our system- recommended images overYahoo-suggested images in learners’ considerations as appropriate image resources to memorize new vocabulary.
Index Terms- Abstract Noun’s Image, Educational Technology, Image Recommender System, Learning Item Creation Tool

learners worldwide are struggling significantly toacquire L2 vocabulary efficiently. Therefore, thepopularity of self-learning systems has increased
rapidly. As the demands increase, a numerous number
of web-based language learning (WBLL) tools andmobile applications have been introduced.

I. INTRODUCTION
In the area of second language acquisition (SLA), it
has been revealed that vocabulary is central tolanguage, and of critical importance to the typicallanguage learners [1]. Knowledge of vocabulary is
also considered as the backbone of learners’
competency that facilitates learning of any languagetasks [2]. Lack of significant L2 vocabulary can be thereason of miscommunication or poor communication.
Significant vocabulary can arguably differentiate thenervous speakers and expert speakers. In addition,
deficiency in vocabulary can make a learner’s learning
process slower. Nevertheless, the teaching andlearning of L2 vocabulary have been undervalued in
the field of SLA. One of the reasons behind is that
SLA researchers and teachers have typicallyprioritized syntax and phonology as “more serious
candidates for theorizing”, more central to linguistic
theory, and more critical to language pedagogy [1].
Quick forgetting of learned vocabulary is anotherinevitable problem that L2 learners often experience.
Research on the learning approaches [3][4][5]revealed that intentional vocabulary learning is the key
for most of the EFL (English as a Foreign Language)
learners’ vocabulary expansion because new words
are difficult and slow to acquire without ambiguity outof context [6]. Learning new vocabulary in theclassroom is considered less effective in theacquisition of intentional vocabulary. As a result, L2

This study is partly supported by a Grant-in-Aid forYoung Scientists (B) of the Japanese Society for the
Promotion of Science (JSPS) under Grant No.
15K20980
Nonetheless, the confusions remain unsolved when it
is a matter of the representation of learning resources.
Several representation methods of learning materials
have been tested previously. Due to the inadequate
IT-skills, learners from non-IT backgrounds are still
struggling to cope with the generation of learningmaterials. In self-learning, learning items that are
generated by using meaningful images are known as
the best learning resources for noun acquisition.
However, finding meaningful still images has been achallenge in CALL (Computer-Assisted Language
Learning) research. A highlighted argument amongst
L2 researchers is to find the educational images for
abstract words. The main issue every researcher faces
is the lack of clearly defined term ‘abstract’ [7].
Natalia [7] also argued that despite numerous
approaches to distinguish between abstract andconcrete nouns; linguists have so far failed to come to

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the unanimous understanding of their categories.
Some words are too abstract that those still remained
unclear to the human mind. Accordingly, finding
appropriate (synonymously educational or suitable)
images for abstract nouns is extremely difficult, andalso a matter of debate. Due to that, to find educational
images, foreign language learners and educators
around the globe are suffering significantly.
To overcome the research gaps, our study proposed an
approach that arguably suggests educational images tothe learners. Furthermore, an on-demand vocabulary
learning system has been developed that assists
learners in self-learning. The principal objective of ourstudy is to assist foreign language learners in theacquisition of foreign language learning by
self-creation of learning items.

Volume-2, Issue-5, May.-2016

autonomous and collaborative vocabulary-learning
environment called SIGMA [13][14] was developed
support PSI’s operations. However, in the process oflearning material creation, all the resources (including
meaning, pronunciation data, and image) needed to beprovided by the user. To our observation,
accumulation of the resources prior to learningmaterial generation is very problematic andtime-consuming, which may lead to slow learning.
Additionally, with respect to learning effectiveness,
their studies [12][13][14] failed to show any
significant differences over traditional methods.
In the context of L2 vocabulary acquisition, theeffectiveness of still images and dynamic videos has
been revealed. Researchers who view on nounacquisition believe that still images are more
appropriate than dynamic videos while acquiring newnouns. Oxford [15] argued that meaningful visual
images make learning more efficient because it helps
learners associating to their memory. The idea of theuniversally accepted images has been introduced by
Kaneko et al. [16]. However, their study [16] did notclarify the nature of the images that can be considered
as universal images.
In our study, we have developed a platform called
AIVAS [17]. The AIVAS generates a 5-second-longlearning item for each noun. AIVAS reducesconsiderable loads on learners/educators byautomatically extracting learning resources from the
web engines upon user demand. Furthermore, AIVAS
generates learning items for 11-languages, which
gives learners to be capable of learning several
languages. AIVAS generates disposable learning
items; therefore, it reduces a significant amount ofloads on databases. Lastly, to solve the difficulties
related to finding educational images for abstract
nouns, a web-based image recommender systemcalled AIVAS-IRA has been implemented. This
article emphasizes on our proposed approach thatextracts appropriate images for abstract nouns.

II. LITERATURE REVIEW
Throughout the last decades, significant amounts ofweb-based learning and mobile learning platforms
have been developed that assist learners in the
self-creation of learning items. PhotoStudy[8] wasdeveloped with the concept of collaborative usage ofthe images that were captured by camera-phones.
However, the researchers acknowledged thatcollaborative policing of the learning materials was
victimized by email spam, and a huge volume ofincorrectly marked up images was found which
proved problematic for learners.
UEVL[9] system was developed with the purpose to
assist students in vocabulary learning by using
ubiquitous technologies. This system used dynamic
video clips to generate learning materials. UEVL wasable to detect student’s locations throughradio-frequency identification and global positioning
system technologies. Yet, the study did not investigate
the learning effects of UEVL system created learningmaterials. A study conducted by Wong et al. [10]tested their mobile assisted content creation approachon school children to acquire Chinese idioms andEnglish prepositions. The study, however, does notsuggest any guidelines on the nature of the still images
that have been used.
Afterward, Orapin et al. [11] developed a systemequipped with a dynamic and interactive interface thatallows vocabulary learners to learn vocabulary byusing a mnemonic technique. The system allows users
to seamlessly browse a collection of foreign words
while suggesting phonetically related words of aknown language for helping the memorization of
unfamiliar languages. However, no significant
differences were noted when compared this approachwith a traditional way (dictionary-based learning) andstatic visualization (images are displayed staticallywithout any special interaction) approaches
respectively.
PSI [12] is a web-based vocabulary learning systemthat has functions of creating English vocabulary
learning materials based on short movies. Later on, an

III. THE STUDY: IMAGE EXTRACTION OF
ABSTRACT NOUNSAppropriate images can arguably help learners inrecalling their old memories and keep the newly
learned words longer than usual. Because of that, theimportance of appropriate images in image-basednoun acquisition is significant. Previously conductedstudies [7][16] pointed out some difficulties involved
in finding right images for abstract nouns. The
researchers agreed that the cultural influence is one of
the main reasons behind that. Another common
problem that researchers often face is the polysemic
behavior of a single noun depending on the context.
While finding the common pattern between different
situations, our study considered the basic use (firstsymbolic meaning) of an abstract noun. That is when
we talk about ‘love’; the first symbolic expression thatcomes to human mind is the affection between two

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International Journal of Management and Applied Science, ISSN: 2394-7926

persons. Therefore, our research consideration was tofind an image, which represents ‘love’ as a symbol ofaffection. Our study also considers the limitations of
c
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Volume-2, Issue-5, May.-2016

This particular section talks about our proposed
strategy that has been targeted for abstract nouns.
Figure 1 shows our research paradigm.
A. What is an Abstract Noun? The Definition
In general definition, the term ‘abstract’ hints ofsomething intangible. Cambridge dictionaries
articulated the definition of an abstract noun as ‘a noun
that refers to a thing that does not as a material object’.
Along with that, many linguists defined the term as
their ways. However, the ambiguities related to thedefinitions remain unclear.
This ongoing investigation defines abstract noun
based on a noun’s abstractness-concreteness value
measured by Allen et al. [19]. A study conducted byAllen et al. [19] scaled the Abstractness-Concreteness(C), Imagery (I), Meaningfulness (m), and Frequency(F) measurement of a total 925 nouns. Their researchscaled 925 nouns on 7-point scales. To state a proper
definition of the abstract noun, our study consideredeach noun’s Abstractness- Concreteness(C) values as
the parameter.
Nouns with Abstractness-Concreteness(C)’s mean (M)value is less or equal than 4, have been considered as
abstract nouns. A noun with C’s mean (M) is morethan 4 were not treated as an abstract noun.
Abstract Nouns ≈ C (M≤4)
The remaining sections talk about the nouns that meet
our definition of abstract nouns. To clarify the
confusions, some examples have been shown in Table
1.
Nouns in ‘⃝’ shown in Table 1 meet the definition ofan abstract noun and are taken into consideration in theextraction of appropriate images. Nouns in ‘Х’, eventhough abstract in the general sense, have not been
considered as abstract nouns. However, those nouns
will certainly be investigated in the future studies. Our
study defined the term ‘abstract’ based on English
abstract nouns’ C-value because the proposed
technical platform searches the corresponding image
for any language’s word into its corresponding
English translation.
B. Targeted Abstract Nouns
Generalization of abstract nouns is very difficult due
to the massive variations of the corresponding images
for each noun. It is also very problematic to establish
common patterns between different images.
Additionally, due to the heavy cultural influences,individual’s view on educational images may vary.
Furthermore, due to the polysemic behavior, thelimitations of the image search engines have been wellreflected in its searching output. Therefore, at the
beginning, determining the targeted noun sets was
essential. The term ‘targeted’ abstract nouns are thosenouns that the hypothesis was made and the system
has been tested with.
This particular investigation has been limited to three
types of abstract nouns. Prior to setting the target
abstract nouns, the nature of abstract nouns has been

Figure 1 Research Paradigm.
Table 1 Example of Abstract Nouns.

Table 2 Targeted and Non-Targeted Abstract Nouns.

technology to determine the appropriateness of animage in the representation of an abstract noun. The
principle objective of our study is to equip AIVAS [17]users with a hassle-free creation of learning items
without relying on any other external image resources.
Therefore, our study worked on a noble approach to
extract appropriate (synonymously educational orsuitable) images for abstract nouns by using our image
re-ranking system.

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well investigated. The three types of target abstract
nouns are, 1) basic abstract nouns that represent social
contexts between humans, 2) abstract nouns that are
related to feeling and/or emotion, and 3) abstract
nouns that represent our social and religious beliefs.
Other types of abstract nouns will be investigated in
the future studies. Table 2 shows some examples of thetargeted nouns and non-targeted abstract nouns.
C. What are Appropriate Images?
Image search outputs given by the standard image
search engines for text-based query especially for
abstract nouns have been well observed. While
analyzing the nature of still images, numerous types ofimages have been observed. In the field ofimage-based learning, massive variations in theimages may create confusion and distraction onlearners’ minds. Our observations on the nature ofsearching outputs given by standard image searchengines are that, while searching images for abstract
nouns, three types of images are often found in the
topmost positions. Our study identifies those types ofimages as ambiguous and inappropriate learning
resources for the purpose of image-based vocabularyacquirement. Our study proposed solutions to those
ambiguous and inappropriate images. The proposedsolutions to those three cases are as follows:
Case 1: A still image that contains textual data(texts/characters) in the image frame is supposedly
inappropriate and ambiguous. Those types of images
often destruct non-native English speakers andlearners with poor English ability. In addition, anumerous number of images contain irrelevantcharacters in the image frame. Suggesting thoseimages may cause learners’ learning process slower
and unattractive. Our study presumes that images
having humanoid expressions or objects in the image
frames can be considered as appropriate replacements
over to the images with textual data.
Case 2: Still images that are visually too abstract are
most likely inappropriate learning resources. Thesetypes of images are often observed in searching
outputs. The term ‘too abstract’ refers to those images
with neither textual data nor concrete expression in theframes. This type of images often creates confusion on
the learner’s mind. The proposed alternatives to those
are the images that contain some level ofhumanoid/object existence in the frame.
Case 3: Another type of inappropriate image is, thestill image that contains multiple objects including theexistence of human, object, animation, and textual
data in the image frame. These types of images, torepresent an abstract noun, can be a reason of
confusion and distraction on the learner’s mind.
Alternatively, images that are simple in nature(possibly containing minimal objects in the frame andless background noise) can be considered asmeaningful appropriate images.
To establish a common pattern between images in all
three cases, our study presents the definition of‘appropriate’ images for any noun that belongs to

Volume-2, Issue-5, May.-2016

targeted abstract noun sets. The definition of an
appropriate image for the targeted abstract nouns is, animage having physical or concrete existence possibly
positioned in the central position in the image frame.
The proposed technological framework has been
designed in the way that the outputs meet thedefinition of appropriate images.
IV. THE TECHNOLOGICAL FRAMEWORK
A. The AIVAS: A Web-based Learning Item
Creation System
A web-based on-demand learning system called the
AIVAS has been introduced, which creates learning
items based on user’s demand. The AIVAS (acronyms
for Appropriate Image-based Vocabulary Acquisition
System) functions include, finding the meaning of anoun, image extraction, voice data extraction, andlearning item creation simultaneously. A complete
description of the architecture of the AIVAS's
frontend and backend implementation, and thefeatures has been released previously [17].
A learning item (synonymously learning material orlearning object) for each noun is a representation of a
5-second-long learning resource that is delivered to
the learners. The components of a learning item are,the meaning of a word to be learned, a corresponding
image, pronunciation data, and the spelling. The
format of the learning items, sample learning items,
and the representation method and necessary
discussions have been published [17][18].
B. AIVAS-IRA: The Image Recommender
SystemAIVAS-IRA is a web-based image recommendersystem that has been designed to assist the users of
AIVAS in the process of learning item creation.
AIVAS-IRA underpinned other existing systems
because the system assists L2 learners/educators byrecommending an appropriate image for each noun
while they create self-learning item. The system also
recommends a certain number of relevant images foreach noun, by which learners can select their own
image preferences if the AIVAS-IRA-extracted image
is not satisfactory.
The pseudo code of the IRA’s early version has beenreleased [17]. In the article [17], a brief discussion on
the algorithm implementation has also been
articulated. However, several modifications have been
done from the early version. In our study, sample‘good’ images accumulation was done based on
experimental data. This can be addressed as one of themain modifications of the current version of thesystem over the early version. Additional functions
have been added to the early version. At present, thesystem recommends images for both basic concreteand basic abstract nouns.
C. AIVAS-IRA Implementation and Output
Testing
Learners suggested sample ‘good’ images have been
accumulated to determine the centroid of images. The

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International Journal of Management and Applied Science, ISSN: 2394-7926

centroid of ‘good’ images is the parameter todetermine the appropriateness of other images. An
image that is closest to the centroid of ‘good’ images is
the most appropriate, and vice versa. An individual’scultural background heavily influences the images for
abstract nouns. Therefore, learners suggested sample
images have been prioritized over authors suggested
images. An experiment with 6 participants has been
conducted to accumulate sample ‘good’ images.
Participants were native speakers of 6-different
languages and bearing very different cultural
backgrounds. All 6-participants in this experiment are
engaged actively in the acquisition of foreign
languages. In the accumulation of sample ‘good'
images, 14-English abstract nouns have been used as
the representative nouns. All 14-English abstract
nouns meet our definition of abstract nouns.
A total of 73 sample ‘good' images for 14 abstract
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Volume-2, Issue-5, May.-2016
Table 4 Result of the Image Evaluation Experiment.

have been accumulated. However, during image
analysis, 5 images had been discarded due to theunacceptable formats of the images. Hence, a total of
68 images have been counted as sample ‘good’ images
and tested the system with those. Table 3 shows thedetails on the participants, nominated words used, andthe number of images accumulated for each word.
To demonstrate an output of the AIVAS-IRA, 8
randomly selected images for the abstract noun ‘idea’
have been downloaded. The reason for using randomly
selected images is the copyright law set by image
search engines. Figure 2 shows the input images(uppermost), the re-ranking environment in commandprompt (in the middle), and the output images inre-ranked order (lowermost), respectively.
The image image0.jpg in the re-ranked folder is theappropriate image found from the 8-corresponding
images that were taken as input for the word ‘idea’.
Therefore, we argue that, for acquiring foreign
vocabulary, the image0.jpg is the best image that canbe used in the generation of a self-learning item for theword ‘idea’.

Table 3 Details on Sample Image Accumulation Experiment.

V. IMAGE EVALUATION EXPERIMENT
D. Method and Material
To assess the AIVAS-IRA-recommended images for
abstract nouns, at first, six English abstract nouns(sadness, pain, recognition, idea, hell, and angel) havebeen selected. Secondly, 16-top ranked images (.jpg
formatted images only) for each noun have been
downloaded and re-ranked them by using
AIVAS-IRA. Google Image Search APIs have beenused as the source of the images that have been
downloaded. Finally, a survey questionnaire has been
prepared by comparing AIVAS-IRA versus Google
versus Yahoo UK suggested top-ranked image foreach noun. The top-ranked images (from both Google
and Yahoo UK) for the selected nouns have beengathered on the same day the outputs have been tested.
It can be mentioned that top-ranked images in both
Google and Yahoo can be changed, periodically.
During the survey, 20 participants were asked to rateeach image on a 5-point scale based on theirassessment of two factors: firstly (Q1),appropriateness (how appropriate) of the image to thecorresponding noun, and secondly (Q2), as a learning
resource to memorize the word. That is, a maximum of
10-points (5-points for each factor) was assigned toeach word. There was no time restriction during thesurvey. That is, the participants could spend as muchtime as they wanted to return the feedback form.

Figure 2 Image-ranking by AIVAS-IRA for an Abstract Noun.

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International Journal of Management and Applied Science, ISSN: 2394-7926

Participants could select from a paper form and an
online form to submit the feedback for the survey
questionnaires. It has been observed that an average of
5 to 7 minutes of time required getting eachparticipant’s feedback.
E. Result
Tukey’s test was used for comparison of the multiple
measurements that have been made during the image
evaluation experiment. Statistical analysis revealed asignificant difference between the AIVAS-IRA andYahoo in the area of Q2 (P=0.04; post-hoc analysis
Tukey’s test). However, no difference is present
between the AIVAS-IRA versus Google in the area of
Q2 (P=0.35). Besides, no significant differences were
noticed between the AIVAS-IRA versus Google, andAIVAS-IRA versus Yahoo in the area of Q1 (P=0.92
and P=0.24, respectively).

Volume-2, Issue-5, May.-2016

may not be a suitable definition for other types ofabstract nouns. We consider that as one of thelimitations of the current work. At this moment, theprototype version of both AIVAS and AIVAS-IRA
may not be visually attractive and user-friendly.
Learners with inadequate IT-skills may find thesystems' operations rather complex. User-friendly
interfaces will be designed before releasing thesoftware to the marketplace.
REFERENCES[1]

C. James, and T. Huckin, “Second language vocabularyacquisition: A rationale for pedagogy,” Cambridge University
Press, 1997.
[2] G. Bahman, "Teaching vocabulary through web-based
language learning (WBLL) approach," Procedia Technology
1, pp. 334-339, 2012.
[3] J. H. Hulstijn, "Retention of inferred and given word
meanings: Experiments in incidental vocabulary learning,"
Vocabulary and applied linguistics, Palgrave Macmillan UK,
pp. 113-125, 1992.
[4] I. S. Nation, “Learning vocabulary in another language,” Ernst
Klett Sprachen, 2001.
[5] L. Batia and Y. Yano, "Understanding unfamiliar words in a
text: Do L2 learners understand how much they don't
understand?” Reading in a Foreign Language 13.2, pp.
549-566, 2001.
[6] W. Qun, "Designing a smartphone app to teach English (L2)
vocabulary," Computers & Education 85, pp. 170-179, 2015.
[7] K. Natalia, "Understanding of abstract nouns in linguistic
disciplines," Procedia-Social and Behavioral Sciences 136, pp.
8-11, 2014.
[8] J. Sam, K. Binsted, and D. Suthers, "PhotoStudy: Vocabulary
learning and collaboration on fixed & mobile devices," IEEE
International Workshop on Wireless and Mobile Technologies
in Education, IEEE, 2005.
[9] H. Yueh-Min, Y. Huang, S. Huang, and Y. Lin, "A ubiquitous
English vocabulary learning system: Evidence ofactive/passive
attitudes
vs.
usefulness/ease-of-use,"
Computers & Education 58, no. 1, pp. 273-282, 2012.
[10] L. H. Wong and C. K. Looi, "Vocabulary learning by
mobile‐assisted authentic content creation and social
meaning‐making: two case studies," Journal of Computer
Assisted Learning 26, no. 5, pp. 421-433, 2010.
[11] A. Orapin, C. He, K. Takashima, T. Leelanupab, and Y.
Kitamura, "Mnemonic-based interactive interface forsecond-language vocabulary learning," Proceedings of the
Human Interface Society, 2014.
[12] K. Hasegawa, S. Amemiya, M. Ishikawa, K. Kaneko, and H.
Miyakoda, “PSI: A system for creating English vocabularymaterials based on short movies,” Information and Systems in
Education, 6, pp. 26-33, 2007.
[13] K. Kaneko, K. Hasegawa, X. Jin, M. Ishikawa, and H.
Miyakoda, “Autonomous and collaborative learning
environment for vocabulary acquirement,” Proceedings of theinternational Conference on Information Technology
Interfaces, pp. 147-152, 2008.
[14] K. Kaneko and H. Miyakoda, “The history of hacking
SIGMA: An autonomous and collaborative vocabulary
learning system,” Proceedings of the International Joint
Conference on Computer Science and Software Engineering(JCSSE), pp. 426-431, 2011.
[15] O. Rebecca and D. Crookall, "Vocabulary learning: A criticalanalysis of techniques," TESL Canada Journal 7.2, pp. 9-30,
1990.
[16] K. Kaneko, K. Hasegawa, S. Amemiya, M. Ishikawa, H.
Miyakoda, and W. Tsukahara, “Multi-linguistic learningmaterials for vocabulary acquirement based on universal
images,” Proceedings of the International Internet Education
Conference, 2007.

VI. DISCUSSION
This article reports on three research questions, firstly,
what kind of still images can be considered as
appropriate learning resources for acquiring abstract
nouns? Secondly, is a technological platform able torecommend appropriate images for abstract nouns?
Thirdly, if so, whether or not those images will
possibly fulfill learners’ expectations? For that reason,
this article presents with i) the definition of abstract
nouns, and ii) the definition of appropriate images forthree-types of abstract nouns. To extract theappropriate images for abstract nouns, an image
recommender system called AIVAS-IRA has been
implemented. The AIVAS-IRA assists AIVAS users
by suggesting the best possible image for each noun tomemorize. Additionally, the image recommendersystem is able to recommend some relevant images tothe learners. By that way, the learners will be helped
significantly to understand the nouns that havemultiple meanings. For example, the Japanese word
金(kin) can be used to express gold, money, metal,
cash and currency. An evaluation experiment with 20
participants of 9 nationalities has been conducted toassess AIVAS-IRA suggested top-ranked images for
abstract nouns. Result revealed a significant difference
between the AIVAS-IRA and Yahoo in the area of Q2(P=0.04; post-hoc analysis Tukey’s test). Hence, thisarticle yield conclusion that, AIVAS-IRA is able torecommend educational images for abstract nouns thatwill help foreign language learners in quick
memorization and long-term memory retention.
VII. LIMITATIONS & FUTURE DIRECTIONS
Few improvements of the ongoing investigation are
under consideration. The assessment of AIVAS-IRArecommended images with respect to both short-termand long-term memory retention will be investigatedin future studies. Our study limits to three particular
types of abstract nouns. The proposed definition of‘appropriate’ images may be a matter of debate and

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International Journal of Management and Applied Science, ISSN: 2394-7926[17] M. N. Hasnine, Y. Hirai, M. Ishikawa, H. Miyakoda, and K.
Kaneko, “A vocabulary learning system by on-demand
creation of multi-linguistic materials based on appropriate
Images,” Proceedings of the International Conference on
e-Commerce, e-Administration, e-Society, e-Education, ande-Technology (e-CASE & e-Tech 2014) – Fall Session, pp.
343-356, 2014.

Volume-2, Issue-5, May.-2016[18] M. N. Hasnine, Y. Hirai, K. Kaneko, M. Ishikawa, and H.
Miyakoda, "Learning effects investigation of an on-demand
vocabulary learning materials creation system based on
appropriate images," Proceedings of the ICT International
Student Project Conference (ICT-ISPC), 2015.
[19] P. Allan, J. C. Yuille, and S. A. Madigan, "Concreteness,imagery, and meaningfulness values for 925 nouns," Journalof experimental psychology 76, 1968.

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