no code implementations • 25 Oct 2023 • Masashi Oshika, Kosuke Yamada, Ryohei Sasano, Koichi Takeda
It has been known to be difficult to generate adequate sports updates from a sequence of vast amounts of diverse live tweets, although the live sports viewing experience with tweets is gaining the popularity.
no code implementations • 23 May 2023 • Kosuke Yamada, Ryohei Sasano, Koichi Takeda
The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill.
no code implementations • 27 Apr 2023 • Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Recent studies have demonstrated the usefulness of contextualized word embeddings in unsupervised semantic frame induction.
1 code implementation • EMNLP 2021 • Kosuke Yamada, Yuta Hitomi, Hideaki Tamori, Ryohei Sasano, Naoaki Okazaki, Kentaro Inui, Koichi Takeda
We also consider a new headline generation strategy that takes advantage of the controllable generation order of Transformer.
no code implementations • ACL 2021 • Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Recent studies on semantic frame induction show that relatively high performance has been achieved by using clustering-based methods with contextualized word embeddings.
no code implementations • Findings (ACL) 2021 • Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Furthermore, we examine the extent to which the contextualized representation of a verb can estimate the number of frames that the verb can evoke.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Kosuke Yamada, Tsutomu Hirao, Ryohei Sasano, Koichi Takeda, Masaaki Nagata
Dividing biomedical abstracts into several segments with rhetorical roles is essential for supporting researchers{'} information access in the biomedical domain.
no code implementations • ACL 2019 • Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors.