24 citations · 28 across the 9 of their papers we have counts for
7 papers · 1 filter
Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset
Taichi Murayama, Shohei Hisada, Makoto Uehara +2
Fake news provokes many societal problems; therefore, there has been extensive research on fake news detection tasks to counter it. Many fake news datasets were constructed as reso…
JaMIE: A Pipeline Japanese Medical Information Extraction System
Fei Cheng, Shuntaro Yada, Ribeka Tanaka +2
We present an open-access natural language processing toolkit for Japanese medical information extraction. We first propose a novel relation annotation schema for investigating the…
Mitigation of Diachronic Bias in Fake News Detection Dataset
Taichi Murayama, Shoko Wakamiya, Eiji Aramaki
Fake news causes significant damage to society.To deal with these fake news, several studies on building detection models and arranging datasets have been conducted. Most of the fa…
Biomedical Entity Linking with Contrastive Context Matching
Shogo Ujiie, Hayate Iso, Eiji Aramaki
We introduce BioCoM, a contrastive learning framework for biomedical entity linking that uses only two resources: a small-sized dictionary and a large number of raw biomedical arti…
End-to-end Biomedical Entity Linking with Span-based Dictionary Matching
Shogo Ujiie, Hayate Iso, Shuntaro Yada +2
Disease name recognition and normalization, which is generally called biomedical entity linking, is a fundamental process in biomedical text mining. Recently, neural joint learning…
Learning to Select, Track, and Generate for Data-to-Text
Hayate Iso, Yui Uehara, Tatsuya Ishigaki +6
We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information…