no code implementations • 21 Dec 2022 • Masaki Asada
The proposed model is trained and evaluated on a widely used data set, and as a result, it is shown that utilizing heterogeneous domain information significantly improves the performance of relation extraction from the literature.
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 • 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 • 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.