Form6(submit in MS Word form)
Grant No.

２０－Ｇ５２

Research Report
Name: Maria Telegina
Organization (at the start of the grant period):
Tokyo University
Title of Research:
Towards an Understanding of the Fundamental Domains of Japanese Mental Lexicon.
Why hima (‘free time’) is bad and ushiro (‘back’) is scary?

Purpose of Research:（200 words）
This project investigates culturally and socially specific features of connections between the concepts in the Japanesemental lexicon based on the crowd-sourced study on word relations and word similarity. The resulting dataset of thisstudy will consist of the descriptions of relations and similarity scores of pairs of associations or words co-occurring in
corpus produced by the volunteers on a crowd-sourcing platform. The resulting dataset will have a potential for
implementation in a number of fields – from lexicography and language learning to natural language processing andunderstanding. I expect this project to contribute to the global discussion on the mental lexicon and to play an importantrole in forming a basis for science-supported Japanese language learning and teaching. This study will not only allow
detection of semantic relations and similarity within the data set validated by native speakers but also will create a novel
form of public outreach and help to form a community of Japanese native-speaking volunteers interested in research on
the Japanese language.
Content/Methodology of Research:（400 words）
This study was conducted as a people-powered study on semantic relations, aiming to obtain information on word
similarity and types of relations. The page of the study and survey work-flows – one on similarity, another one on word
relations were created via the online citizen science platform Zooniverse. As the first step of the project, a set of pairs ofword associations was uploaded to the platform. The first set consisted of associations tagged as culturally specifictogether with hapax legomena (individual associations) from my doctoral project dataset. As the study progressed newassociation pairs were added using the newly processed data from the Japanese version of the Small World of Words
project - a free-word association data collection project we launched in parallel with this project. In total, more than 2000
pairs of word associations were added to the data set and annotated by the volunteers. The previous studies(e.g.,Vankrunkelsven et al., 2018) suggest that the simultaneous analysis of word association data and co-occurrence-based
data gives the fullest picture of the mental lexicon. Therefore, further in the project words frequently co-occurring withthe stimuli will be also added to the stimuli set from the corpus.
Participants, volunteering to take part in the study first choose the workflow they want to participate in – word similarity
or word relations, then they read through a tutorial explaining the process of the annotation. They are also instructed thatthey can choose a number of pairs of words they are willing to annotate. In word similarity workflow, the participants
are shown one word pair at a time and asked to evaluate their similarity on a scale from 0 (not similar) to 6 (very similar).
In the relations workflow, participants are shown one word pair at a time and asked to choose or to describe the relations
between two words. The set of choices that participants are given consists of 12 options: similar meaning (synonym),
opposite meaning (antonym), part, material, actions, superior concept, subordinate concept, environment, attribute,culturally specific concept, other (with an option to describe in their own words), not known.
The study is still ongoing, but the resulting data are in the process of qualitative analysis and quantitative analyses (DeDeyne et al., 2016).

De Deyne, S., Perfors, A., & Navarro, D. J. (2016). Predicting human similarity judgments with distributional models: The
value of word associations. Proceedings of COLING 2016, the 26th International Conference on Computational
Linguistics: Technical Papers, 1861–1870.
Vankrunkelsven, H., Verheyen, S., Storms, G., & De Deyne, S. (2018). Predicting lexical norms: A comparison between a word
association model and text-based word co-occurrence models. Journal of Cognition, 1(1).

Conclusion/Observation（200 words）
To
conduct
thestudy,I
havecreatedtworesources:
adata
collection
page(https://www.zooniverse.org/projects/mariatelegina/go-to-go)
andaFacebookpage(https://www.facebook.com/kotoba.no.kankei) to promote the project, discuss it with the citizen scientists, and publish
updates and news.
The study is still ongoing, but in the process of the data collection 3 stimulus sets (2,191-word pairs in total) wereannotated, and the community of the citizen science annotators was formed. On the data collection page, 43 registeredvolunteers and unregistered volunteers made 68, 480 classifications and completed annotation of both similarity measure
and word relations for 2,191 subjects (Fig. 1).
Overall, 132,111 people have seen promotion materials from the Facebook page at least once. Most participants (around60%) are female participants. The main age ranges of the participants are from the mid-20s to mid-30s and from mid30s to mid-40s.
I am currently analysing the word relations responses for the first stimulus set (pairs of most and least frequent word
associations from my doctoral project, 1000 pairs of time and space-related vocabulary). The total number of responses
is 9500. The most frequent relations detected in this data set are attribute, part, environment, synonymity, andsubordination.

Fig. 1
https://www.zooniverse.org/projects/mariatelegina/go-to-go on Dec 22, 2021