Search Results for author:
Found 31 papers, 8 papers with code
Annotation-Scheme Reconstruction for “Fake News” and Japanese Fake News Dataset
Using the annotation scheme, we construct and publish the first Japanese fake news dataset.
Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations
Afterwards, a correlation analysis between the extracted emotions and a set of vaccination measures in Japan was conducted. The results revealed that surprise and fear were the most intense emotions predicted by the model for writers and readers, respectively, on the vaccine-related Tweet dataset.
JaMIE: A Pipeline Japanese Medical Information Extraction System with Novel Relation Annotation
In this paper, we first propose a novel relation annotation schema for investigating the medical and temporal relations between medical entities in Japanese medical reports.
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world.
Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset
Using the annotation scheme, we construct and publish the first Japanese fake news dataset.
JaMIE: A Pipeline Japanese Medical Information Extraction System
We present an open-access natural language processing toolkit for Japanese medical information extraction.
Mitigation of Diachronic Bias in Fake News Detection Dataset
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.
Single Model for Influenza Forecasting of Multiple Countries by Multi-task Learning
The accurate forecasting of infectious epidemic diseases such as influenza is a crucial task undertaken by medical institutions.
Biomedical Entity Linking with Contrastive Context Matching
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 articles.
End-to-end Biomedical Entity Linking with Span-based Dictionary Matching
Disease name recognition and normalization, which is generally called biomedical entity linking, is a fundamental process in biomedical text mining.
KART: Parameterization of Privacy Leakage Scenarios from Pre-trained Language Models
One problem is that previous studies have assessed the risk for different real-world privacy leakage scenarios and attack methods, which reduces the portability of the findings.
Offensive Language Detection on Video Live Streaming Chat
To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i. e., transfer learning).
Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases
Applying natural language processing (NLP) to medical and clinical texts can bring important social benefits by mining valuable information from unstructured text.
Syndromic surveillance using search query logs and user location information from smartphones against COVID-19 clusters in Japan
Second, we extracted the location of the WSSCI via the smartphone application.
NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset
This has affected the social life of people owing to enforcements, such as "social distancing" and "stay at home."
Learning to Select, Track, and Generate for Data-to-Text
We propose a data-to-text generation model with two modules, one for tracking and the other for text generation.
Multivariate Linear Regression of Symptoms-related Tweets for Infectious Gastroenteritis Scale Estimation
Based on these results, we can infer that social sensors can reliably detect unseasonal and local disease events under certain conditions, just as they can for seasonal or global events.
Density Estimation for Geolocation via Convolutional Mixture Density Network
Nowadays, geographic information related to Twitter is crucially important for fine-grained applications.
Detecting Japanese Patients with Alzheimer's Disease based on Word Category Frequencies
This study investigates features of the words that AD patients use in their spoken language.
MedNLPDoc: Japanese Shared Task for Clinical NLP
Due to the recent replacements of physical documents with electronic medical records (EMR), the importance of information processing in medical fields has been increased.
Forecasting Word Model: Twitter-based Influenza Surveillance and Prediction
Because of the increasing popularity of social media, much information has been shared on the internet, enabling social media users to understand various real world events.
