no code implementations • BioNLP (ACL) 2022 • Naoki Iinuma, Makoto Miwa, Yutaka Sasaki
To overcome this issue, our methods indirectly utilize distant supervision data with manually annotated training data.
no code implementations • 6 Jun 2024 • Kohei Makino, Makoto Miwa, Yutaka Sasaki
This study addresses a crucial challenge in instance-based relation extraction using text generation models: end-to-end training in target relation extraction task is not applicable to retrievers due to the non-differentiable nature of instance selection.
no code implementations • 28 Sep 2021 • Hayato Futase, Tomoki Tsujimura, Tetsuya Kajimoto, Hajime Kawarazaki, Toshiyuki Suzuki, Makoto Miwa, Yutaka Sasaki
Furthermore, it is difficult to generate the changes at a specific timing and they often do not match with actual changes.
1 code implementation • Findings (ACL) 2021 • Kohei Makino, Makoto Miwa, Yutaka Sasaki
In this paper, we propose a novel edge-editing approach to extract relation information from a document.
1 code implementation • 24 Oct 2020 • Masaki Asada, Makoto Miwa, Yutaka Sasaki
Specifically, we focus on drug description and molecular structure information as the drug database information.
no code implementations • LREC 2020 • Kyosuke Yamaguchi, Ryoji Asahi, Yutaka Sasaki
This paper describes a novel corpus tailored for the text mining of superconducting materials in Materials Informatics (MI), named SuperConductivety Corpus for Materials Informatics (SC-CoMIcs).
no code implementations • LREC 2020 • Savong Bou, Naoki Suzuki, Makoto Miwa, Yutaka Sasaki
In contrast, in our OSR annotation, a relation is annotated as a relation mention (i. e., not a link but a node) and domain and range links are annotated from the relation mention to its argument entity mentions.
no code implementations • ACL 2018 • Masaki Asada, Makoto Miwa, Yutaka Sasaki
We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information.
no code implementations • IJCNLP 2017 • Yota Toyama, Makoto Miwa, Yutaka Sasaki
We propose a novel method that exploits visual information of ideograms and logograms in analyzing Japanese review documents.
no code implementations • IJCNLP 2017 • Satoshi Yawata, Makoto Miwa, Yutaka Sasaki, Daisuke Hara
We define a fine-grained feature set based on the hand-coded syllables and train a logistic regression classifier on labeled syllables, expecting to find the discriminative features from the trained classifier.
no code implementations • WS 2017 • Masaki Asada, Makoto Miwa, Yutaka Sasaki
We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model.
no code implementations • SEMEVAL 2017 • Tomoki Tsujimura, Makoto Miwa, Yutaka Sasaki
This paper describes our TTI-COIN system that participated in SemEval-2017 Task 10.
no code implementations • 16 Jun 2017 • Takuma Yoneda, Koki Mori, Makoto Miwa, Yutaka Sasaki
We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility.
no code implementations • EACL 2017 • Takuma Yoneda, Koki Mori, Makoto Miwa, Yutaka Sasaki
We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility.
no code implementations • COLING 2016 • Josuke Yamane, Tomoya Takatani, Hitoshi Yamada, Makoto Miwa, Yutaka Sasaki
Most of the recent hypernym detection models focus on a hypernymy classification problem that determines whether a pair of words is in hypernymy or not.
no code implementations • 11 Aug 2015 • Heejin Choi, Yutaka Sasaki, Nathan Srebro
We present improved methods of using structured SVMs in a large-scale hierarchical classification problem, that is when labels are leaves, or sets of leaves, in a tree or a DAG.