515 citations · 873 across the 12 of their papers we have counts for
12 papers
Enhancing Label Consistency on Document-level Named Entity Recognition
Minbyul Jeong, Jaewoo Kang
Named entity recognition (NER) is a fundamental part of extracting information from documents in biomedical applications. A notable advantage of NER is its consistency in extractin…
BERN2: an advanced neural biomedical named entity recognition and normalization tool
Mujeen Sung, Minbyul Jeong, Yonghwa Choi +3
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical enti…
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
Seongjun Yun, Minbyul Jeong, Sungdong Yoo +5
Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful representations of graph-structured data. Despite the success of GNNs, most existing G…
Regularization for Long Named Entity Recognition
Minbyul Jeong, Jaewoo Kang
When performing named entity recognition (NER), entity length is variable and dependent on a specific domain or dataset. Pre-trained language models (PLMs) are used to solve NER ta…
Pandemics are catalysts of scientific novelty: Evidence from COVID-19
Meijun Liu, Yi Bu, Chongyan Chen +14
Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to…
Transferability of Natural Language Inference to Biomedical Question Answering
Minbyul Jeong, Mujeen Sung, Gangwoo Kim +4
Biomedical question answering (QA) is a challenging task due to the scarcity of data and the requirement of domain expertise. Pre-trained language models have been used to address…
Answering Questions on COVID-19 in Real-Time
Jinhyuk Lee, Sean S. Yi, Minbyul Jeong +5
The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to…
Building a PubMed knowledge graph
Jian Xu, Sunkyu Kim, Min Song +12
PubMed is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguated, which has significantly hindered knowledge discovery.…
Learning by Semantic Similarity Makes Abstractive Summarization Better
Wonjin Yoon, Yoon Sun Yeo, Minbyul Jeong +2
By harnessing pre-trained language models, summarization models had rapid progress recently. However, the models are mainly assessed by automatic evaluation metrics such as ROUGE.…
Graph Transformer Networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim +2
Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved state-of-the-art performance in tasks such as node classification and link pred…
Pre-trained Language Model for Biomedical Question Answering
Wonjin Yoon, Jinhyuk Lee, Donghyeon Kim +2
The recent success of question answering systems is largely attributed to pre-trained language models. However, as language models are mostly pre-trained on general domain corpora…
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
Raehyun Kim, Chan Ho So, Minbyul Jeong +3
Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices…