1 code implementation • 1 Mar 2024 • Taichi Aida, Danushka Bollegala
Detecting temporal semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions.
1 code implementation • 16 Oct 2023 • Taichi Aida, Danushka Bollegala
Intuitively, if the meaning of $w$ does not change between $\mathcal{C}_1$ and $\mathcal{C}_2$, we would expect the distributions of contextualised word embeddings of $w$ to remain the same before and after this random swapping process.
1 code implementation • 16 Oct 2023 • Xiaohang Tang, Yi Zhou, Taichi Aida, Procheta Sen, Danushka Bollegala
Given this relationship between WSD and SCD, we explore the possibility of predicting whether a target word has its meaning changed between two corpora collected at different time steps, by comparing the distributions of senses of that word in each corpora.
1 code implementation • 15 May 2023 • Taichi Aida, Danushka Bollegala
However, some of the previously associated meanings of a target word can become obsolete over time (e. g. meaning of gay as happy), while novel usages of existing words are observed (e. g. meaning of cell as a mobile phone).
no code implementations • LREC 2022 • Daisuke Suzuki, Yujin Takahashi, Ikumi Yamashita, Taichi Aida, Tosho Hirasawa, Michitaka Nakatsuji, Masato Mita, Mamoru Komachi
Therefore, in this study, we created a quality estimation dataset with manual evaluation to build an automatic evaluation model for Japanese GEC.
no code implementations • ACL 2021 • Seiichi Inoue, Taichi Aida, Mamoru Komachi, Manabu Asai
In this study, we propose a model that extends the continuous space topic model (CSTM), which flexibly controls word probability in a document, using pre-trained word embeddings.