no code implementations • COLING 2022 • Yoshifumi Kawasaki, Maëlys Salingre, Marzena Karpinska, Hiroya Takamura, Ryo Nagata
This article revisits statistical relationships across Romance cognates between lexical semantic shift and six intra-linguistic variables, such as frequency and polysemy.
1 code implementation • EMNLP 2021 • Kazuaki Hanawa, Ryo Nagata, Kentaro Inui
To shed light on these points, we investigate a wider range of methods for generating many feedback comments in this study.
no code implementations • INLG (ACL) 2021 • Ryo Nagata, Masato Hagiwara, Kazuaki Hanawa, Masato Mita, Artem Chernodub, Olena Nahorna
In this paper, we propose a generation challenge called Feedback comment generation for language learners.
no code implementations • 17 Dec 2024 • Ryo Kishino, Hiroaki Yamagiwa, Ryo Nagata, Sho Yokoi, Hidetoshi Shimodaira
Lexical semantic change detection aims to identify shifts in word meanings over time.
no code implementations • 19 May 2023 • Ryo Nagata, Hiroya Takamura, Naoki Otani, Yoshifumi Kawasaki
In this paper, we propose methods for discovering semantic differences in words appearing in two corpora based on the norms of contextualized word vectors.
no code implementations • Findings (ACL) 2022 • Ryo Nagata, Manabu Kimura, Kazuaki Hanawa
In this paper, we explore the capacity of a language model-based method for grammatical error detection in detail.
1 code implementation • COLING 2020 • Takumi Gotou, Ryo Nagata, Masato Mita, Kazuaki Hanawa
The performance measures are based on the simple idea that the more systems successfully correct an error, the easier it is considered to be.
no code implementations • LREC 2020 • Ryo Nagata, Kentaro Inui, Shin{'}ichiro Ishikawa
In this paper, we report on datasets that we created for research in feedback comment generation {---} a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning.
no code implementations • IJCNLP 2019 • Ryo Nagata
We have tested three baseline methods on the dataset, showing that a simple neural retrieval-based method sets a baseline performance with an F-measure of 0. 34 to 0. 41.
no code implementations • WS 2019 • Tomoya Mizumoto, Hiroki Ouchi, Yoriko Isobe, Paul Reisert, Ryo Nagata, Satoshi Sekine, Kentaro Inui
This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts.
no code implementations • NAACL 2019 • Masato Mita, Tomoya Mizumoto, Masahiro Kaneko, Ryo Nagata, Kentaro Inui
This study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models.
no code implementations • WS 2018 • Ryo Nagata, Tomoya Mizumoto, Yuta Kikuchi, Yoshifumi Kawasaki, Kotaro Funakoshi
Based on the discussion of possible causes of POS tagging errors in learner English, we show that deep neural models are particularly suitable for this.
no code implementations • COLING 2018 • Ryo Nagata, Taisei Sato, Hiroya Takamura
This paper introduces and examines the hypothesis that lexical richness measures become unstable in learner English because of spelling errors.
no code implementations • WS 2017 • Tomoya Mizumoto, Ryo Nagata
Part-of-speech (POS) tagging and chunking have been used in tasks targeting learner English; however, to the best our knowledge, few studies have evaluated their performance and no studies have revealed the causes of POS-tagging/chunking errors in detail.
no code implementations • EACL 2017 • Hiroya Takamura, Ryo Nagata, Yoshifumi Kawasaki
We analyze semantic changes in loanwords from English that are used in Japanese (Japanese loanwords).
no code implementations • LREC 2016 • Hiroya Takamura, Ryo Nagata, Yoshifumi Kawasaki
We address the task of automatically estimating the missing values of linguistic features by making use of the fact that some linguistic features in typological databases are informative to each other.
1 code implementation • 26 May 2014 • Toshiya Namikawa, Ryo Nagata
The lensing signals involved in CMB polarization maps have already been measured with ground-based experiments such as SPTpol and POLARBEAR, and would become important as a probe of cosmological and astrophysical issues in the near future.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics