This study examined quantitative and qualitative changes in vocabulary production among Japanese learners through a paragraph writing activity conducted at a university in China. It also compared evaluations by human teachers and GenAI (ChatGPT and DeepSeek). Morphological analyses showed a gradual increase in learners’ vocabulary use and sentence length, particularly noting an increase in the use of native Japanese words (Wago). Qualitative analyses indicated that human teachers provided the most flexible and nuanced assessments, effectively capturing subtle differences in learners’ vocabulary choices and emotional expressions. While the GenAI assessments correlated with human evaluations, ChatGPT tended to provide cautious and conservative ratings, limiting its ability to capture subtle expressive nuances. DeepSeek’s assessments were somewhat closer to those of human teachers but still lacked sufficient flexibility. These findings highlight the importance of human empathy and nuanced understanding in assessing vocabulary skills, underscoring that effective evaluation should integrate attention to learners’ emotional expressions and personal growth in addition to quantitative measures.

This study examines four paraphrastic connective expressions in Japanese: sunawachi, tsumari, yosuruni, and yō wa, through a quantitative and qualitative investigation of their frequencies across three distinct genres: books, lectures, and casual conversations. The analysis reveals statistically significant differences among the genres, showing that sunawachi is predominantly used in books, tsumari appears frequently in both books and lectures, yosuruni is common in lectures and casual conversations, and yō wa is especially prominent in casual conversations. A subsequent qualitative examination indicates that a simple binary classification of written versus spoken language, defined solely by textual or auditory media, is inadequate to explain these variations. Instead, this paper adopts Chafe’s concepts of Detachment and Involvement to elucidate how these paraphrastic connectives are employed, suggesting that multiple, interrelated factors shape their use in both written and spoken contexts.

Technical Japanese has been predominantly examined within the context of expert communities. However, its use in communication with the general public remains underexplored. This study aims to clarify the language patterns of technical Japanese in these two distinct contexts. Specifically, the study considers Japanese texts from scientific and technical academic journals as representative of expert communities, while texts from note (a Japanese blog platform) are regarded as representative of writing for the general public. After collecting both types of texts, a multi-dimensional analysis was conducted to examine their language patterns. As a result, the findings indicate that academic journals tend to contain more abstract expressions, as well as kanji expressions. In contrast, the language usage on note is more likely to exhibit interactive and subjective expressions, narrative expressions, and plain and polite expressions.

This study aims to clarify the types and usage frequency of basic formulaic language in written Japanese by analyzing the publication sub-corpus from the Balanced Corpus of Contemporary Written Japanese. We used the n-gram model to analyze high-frequency n-grams ranging from 3-grams to 7-grams. The results reveal that the formulaic language common across the three genres largely consist of patterns and combinations of multiple content words, with 3-grams and 4-grams occupying a significant proportion. Furthermore, many of these expressions are categorized as collocations. Additionally, formulaic language consisting of two or more content words tend to feature combinations of [noun/particle/verb], which express more detailed meanings. Finally, in terms of the proportion of formulaic language, fixed expressions such as collocations are found to occupy a high percentage, while semi-fixed expressions, such as patterns, are also present to a notable degree. These findings suggest that both the regularity and flexibility of vocabulary coexist in written Japanese.

In everyday Japanese, vowel sequences such as [ai] potentially undergo coalescence, resulting in the ee-form. While studies have explored this language variation, including dialect-focused studies as well as a case-specific investigation, comprehensive analyses of its factors remain insufficient. This study analyzed the factors influencing the ai-ee form by extracting data from the Corpus of Everyday Japanese Conversation. Statistical analyses, such as regression analysis, were also employed. The findings revealed that the speaker’s sex played a crucial role in predicting the appearance of the ee-form. Particularly, the ee-form tends to emerge in male speakers’ utterances. Regarding intra-linguistic factors, the parts of speech acted as the most influential variable, with adjectives and auxiliary verbs likely to undergo the ee-form.

The International Quantitative Linguistics Conference 2025 (QUALICO 2025) was held at the Masaryk University from June 26 to June 28, 2025. It was organized by the Department of Czech Language at the Faculty of Arts, Masaryk University, together with the International Quantitative Linguistics Association (IQLA). Three keynote lecturers were invited, and 34 papers for talks and five papers for a poster session were presented. Participants from 18 countries attended, including EU countries such as the Czech Republic, Spain, and Italy, as well as Canada, the U.S., Brazil, China, and Japan. Of the 34 papers, six were presented by Japanese researchers or by a foreign student studying in Japan. Additionally, a paper dealing with Japanese data was presented by researchers whose native languages are not Japanese. Most papers focused on classical or fundamental topics such as quantitative indices and valency theory; linguistic laws such as the Menzerath-Altmann’s law and stylometry (including authorship attribution); and large language models (LLMs). Several research presentations focused on AI. The premise of research in this field is the growing presence of AI-generated text in society and academia. Studies have examined how this affects human society and politics, including developing methods for AI detection (identifying whether content was generated by AI) and comparing corpora containing AI-generated texts with those composed of human-authored texts. The IQLA Council Business Meeting was held and new board members were selected. The next conference in Barcelona has been planned for 2027. A call for papers for the next conference will be announced on IQLA’s website.