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Vocabulary plays an important part in second language learning and there are many existing techniques to facilitate word acquisition. One of these methods is code-switching, or mixing the vocabulary of two languages in one sentence. In this paper the authors propose an experimental system for computer-assisted English vocabulary learning in context using a code-switching based approach for Japanese learners. First they introduce the CO-MIX system, an English vocabulary teaching system that uses code-switching for vocabulary acquisition. Next, they show how they utilize incidental learning techniques with graded readers to increase language proficiency. The authors present the system architecture, underlying technologies, and evaluate the system's performance through user interaction with both a baseline and the proposed system by using a semantic differential scale. They also perform separate factor analysis of participants' attitudes for both systems, an analysis of users' mistakes and compare users' language tests scores. Finally, the authors discuss the evaluation results and further development of this technology.

SAMURAI (NIMS 2022), a directory service of the National Institute for Materials Science (NIMS) researchers in Japan was launched in 2009 following the development of NIMS institutional repository (Tanifuji et al. 2019). The concept is to synchronize between profile information of researchers and their publications which are self-archived in the repository system. The SAMURAI was renewed in 2017 with interoperable functions with ORCID. SAMURAI supports various links to not only individual articles and patents, but also to databases such as KAKEN (Database of Grants-in-Aid for Scientific Research by NII). The service has yielded fully identified authors of journal articles from research members of NIMS by implementing a unique ResearcherID. Through this directory, NIMS is promoting materials research, supporting management of its researchers activities, and introducing NIMS researchers and their work to the public. In this work, we present an application to describe each researcher's output topics automatically from the archived research papers in the repository, by implementing materials science specific natural language processing developed in our study (Dieb et al. 2021) that visualizes the research trend of each SAMURAI researchers. The approach can maximize information absorbance for general audience and fully corresponds to open science policy. A list of publications' digital object identifiers (DOIs DOI 2022) for each researcher was constructed from his profile in SAMURAI. (In SAMURAI, the DOIs are stored in a PostgreSQL database). Using the DOI, recent publications were retrieved from NIMS text data mining platform (TDM-PF) in their XML format which were mainly available from 2003. Representative topic terms from their research publications that are related to materials science and engineering were extracted. We utilize term frequency analysis and automatic extraction for materials names to extract these necessary informative terms. Additionally, domain knowledge resources such as dictionaries were used. Data was preprocessed using noise reduction such as removing general English language stop words and physical units filtering. Such words do not have significance on their own. Word cloud approach was used for visualization (Fig. 1). This work brings us an opportunity to apply our NLP experience to mine information from research papers for public knowledge as a step towards data-driven materials science. [ABSTRACT FROM AUTHOR]

Current medical science has not yet found a cure for dementia. The most important measures to combat dementia are to detect the tendency toward cognitive decline as early as possible and to intervene at an early stage. For this reason, screening for dementia based on language ability has attracted much attention in recent years. However, in most of the previous studies, the cohort of people with dementia has been smaller than the control cohort. In this paper, we use a pre-trained Japanese language model for text analysis and evaluate the effectiveness of text augmentation on a dataset consisting of Japanese-speaking healthy older adults and those with mild cognitive impairment (MCI). We also examined what tasks contributed to the results. This experimental setting can also be used to detect other diseases that may affect the language areas of the brain outside of the hospital. [ABSTRACT FROM AUTHOR]

Natural disasters are events that humans cannot control, and Japan has suffered from many such disasters over its long history. Many of these have caused severe damage to human lives and property. These days, numerous Japanese people have gained considerable experience preparing for disasters and are now striving to predict the effects of disasters using social network services (SNSs) to exchange information in real time. Currently, Twitter is the most popular and powerful SNS tool used for disaster response in Japan because it allows users to exchange and disseminate information quickly. However, since almost all of the Japanese-related content is also written in the Japanese language, which restricts most of its benefits to Japanese people, we feel that it is necessary to create a disaster response system that would help people who do not understand Japanese. Accordingly, this paper presents the framework of a question-answering (QA) system that was developed using a Twitter dataset containing more than nine million tweets compiled during the Osaka North Earthquake that occurred on 18 June 2018. We also studied the structure of the questions posed and developed methods for classifying them into particular categories in order to find answers from the dataset using an ontology, word similarity, keyword frequency, and natural language processing. The experimental results presented herein confirm the accuracy of the answer results generated from our proposed system. [ABSTRACT FROM AUTHOR]

Ainu is a critically endangered language spoken by the native inhabitants of northern Japan. This paper describes our research aimed at the development of technology for automatic processing of text in Ainu. In particular, we improved the existing tools for normalizing old transcriptions, word segmentation, and part-of-speech tagging. In the experiments we applied two Ainu language dictionaries from different domains (literary and colloquial) and created a new data set by combining them. The experiments revealed that expanding the lexicon had a positive impact on the overall performance of our tools, especially with test data unrelated to any of the training sets used. [ABSTRACT FROM AUTHOR]

Featured Application: This research crawled a bilingual Japanese-Chinese corpus of a certain size through websites. As a necessary resource for Japanese-Chinese neural machine translation (NMT), it is beneficial for researchers to promote the progress of Japanese-Chinese language-related natural language processing research. Specifically, topics include comparative analysis of grammar, comparative studies of Chinese and Japanese languages, compilation of dictionaries, etc. This will have great significance and contribution to the cultural exchange and industrial cooperation between China and Japan. It also has important theoretical significance and application value to the industrialization of Japanese-Chinese machine translation. In addition, the application of this research will be of great significance in strengthening civil communication and enhancing mutual understanding between China and Japan, as the current Chinese and Japanese relations are not well perceived by the citizens of both countries. We hope that the construction and pathways of the Japanese-Chinese bilingual corpus in this research will help to solve the problem of language barriers in Japanese-Chinese people-to-people communication and mutual understanding. We offer the WCC-JC as a free download under the premise that it is intended for research purposes only. Currently, there are only a limited number of Japanese-Chinese bilingual corpora of a sufficient amount that can be used as training data for neural machine translation (NMT). In particular, there are few corpora that include spoken language such as daily conversation. In this research, we attempt to construct a Japanese-Chinese bilingual corpus of a certain scale by crawling the subtitle data of movies and TV series from the websites. We calculated the BLEU scores of the constructed WCC-JC (Web Crawled Corpus—Japanese and Chinese) and the other compared corpora. We also manually evaluated the translation results using the translation model trained on the WCC-JC to confirm the quality and effectiveness. [ABSTRACT FROM AUTHOR]

Background: Internalizing mental illnesses associated with psychological distress are often underdetected. Text-based detection using natural language processing (NLP) methods is increasingly being used to complement conventional detection efforts. However, these approaches often rely on self-disclosure through autobiographical narratives that may not always be possible, especially in the context of the collectivistic Japanese culture. Objective: We propose the use of narrative writing as an alternative resource for mental illness detection in youth. Accordingly, in this study, we investigated the textual characteristics of narratives written by youth with psychological distress; our research focuses on the detection of psychopathological tendencies in written imaginative narratives. Methods: Using NLP tools such as stylometric measures and lexicon-based sentiment analysis, we examined short narratives from 52 Japanese youth (mean age 19.8 years, SD 3.1) obtained through crowdsourcing. Participants wrote a short narrative introduction to an imagined story before completing a questionnaire to quantify their tendencies toward psychological distress. Based on this score, participants were categorized into higher distress and lower distress groups. The written narratives were then analyzed using NLP tools and examined for between-group differences. Although outside the scope of this study, we also carried out a supplementary analysis of narratives written by adults using the same procedure. Results: Youth demonstrating higher tendencies toward psychological distress used significantly more positive (happiness-related) words, revealing differences in valence of the narrative content. No other significant differences were observed between the high and low distress groups. Conclusions: Youth with tendencies toward mental illness were found to write more positive stories that contained more happiness-related terms. These results may potentially have widespread implications on psychological distress screening on online platforms, particularly in cultures such as Japan that are not accustomed to self-disclosure. Although the mechanisms that we propose in explaining our results are speculative, we believe that this interpretation paves the way for future research in online surveillance and detection efforts. [ABSTRACT FROM AUTHOR]