7.5k citations · 7.7k across the 12 of their papers we have counts for
12 papers
Using tournaments to calculate AUROC for zero-shot classification with LLMs
WonJin Yoon, Ian Bulovic, Timothy A. Miller
Large language models perform surprisingly well on many zero-shot classification tasks, but are difficult to fairly compare to supervised classifiers due to the lack of a modifiabl…
Aspect-Oriented Summarization for Psychiatric Short-Term Readmission Prediction
WonJin Yoon, Boyu Ren, Spencer Thomas +4
Recent progress in large language models (LLMs) has enabled the automated processing of lengthy documents even without supervised training on a task-specific dataset. Yet, their ze…
Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework
Wonjin Yoon, Richard Jackson, Elliot Ford +2
In order to assist the drug discovery/development process, pharmaceutical companies often apply biomedical NER and linking techniques over internal and public corpora. Decades of s…
Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles
Hyunjae Kim, Mujeen Sung, Wonjin Yoon +2
This paper is a technical report on our system submitted to the chemical identification task of the BioCreative VII Track 2 challenge. The main feature of this challenge is that th…
Sequence tagging for biomedical extractive question answering
Wonjin Yoon, Richard Jackson, Aron Lagerberg +1
Current studies in extractive question answering (EQA) have modeled the single-span extraction setting, where a single answer span is a label to predict for a given question-passag…
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…
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.…
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…
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim +4
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting va…
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
Wonjin Yoon, Chan Ho So, Jinhyuk Lee +1
Background: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical…