no code implementations • 5 Apr 2024 • Hwiyeol Jo, Taiwoo Park, Nayoung Choi, Changbong Kim, Ohjoon Kwon, Donghyeon Jeon, Hyunwoo Lee, Eui-Hyeon Lee, Kyoungho Shin, Sun Suk Lim, Kyungmi Kim, Jihye Lee, Sun Kim
Although there has been a growing interest among industries to integrate generative LLMs into their services, limited experiences and scarcity of resources acts as a barrier in launching and servicing large-scale LLM-based conversational services.
no code implementations • 4 Mar 2024 • Yinhua Piao, Sangseon Lee, Yijingxiu Lu, Sun Kim
Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts.
1 code implementation • 16 May 2023 • Nayeon Kim, Yinhua Piao, Sun Kim
Directly adopting existing hypergraph methods on clinical notes cannot sufficiently utilize the hierarchy information of the patient, which can degrade clinical semantic information by (1) frequent neutral words and (2) hierarchies with imbalanced distribution.
no code implementations • 16 Sep 2022 • Sangseon Lee, Dohoon Lee, Yinhua Piao, Sun Kim
In this paper, we propose Structure Prototype Guided Pooling (SPGP) that utilizes prior graph structures to overcome the limitation.
no code implementations • 26 May 2022 • MinGyu Choi, Wonseok Shin, Yijingxiu Lu, Sun Kim
Recent contrastive learning methods have shown to be effective in various tasks, learning generalizable representations invariant to data augmentation thereby leading to state of the art performances.
1 code implementation • 13 Dec 2021 • Yinhua Piao, Sangseon Lee, Dohoon Lee, Sun Kim
To address these challenges, we propose a novel GNN-based sparse structure learning model for inductive document classification.
no code implementations • 26 Apr 2021 • Cheng Wang, Sun Kim, Taiwoo Park, Sajal Choudhary, Sunghyun Park, Young-Bum Kim, Ruhi Sarikaya, Sungjin Lee
We have been witnessing the usefulness of conversational AI systems such as Siri and Alexa, directly impacting our daily lives.
1 code implementation • 24 Apr 2020 • Rezarta Islamaj, Dongseop Kwon, Sun Kim, Zhiyong Lu
Manually annotated data is key to developing text-mining and information-extraction algorithms.
1 code implementation • 23 Dec 2019 • Qingyu Chen, Kyubum Lee, Shankai Yan, Sun Kim, Chih-Hsuan Wei, Zhiyong Lu
Capturing the semantics of related biological concepts, such as genes and mutations, is of significant importance to many research tasks in computational biology such as protein-protein interaction detection, gene-drug association prediction, and biomedical literature-based discovery.
no code implementations • 6 Sep 2019 • Qingyu Chen, Jingcheng Du, Sun Kim, W. John Wilbur, Zhiyong Lu
For the post challenge, the performance of both Random Forest and the Encoder Network was improved; in particular, the correlation of the Encoder Network was improved by ~13%.
no code implementations • 26 Feb 2018 • Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu
Publications in the life sciences are characterized by a large technical vocabulary, with many lexical and semantic variations for expressing the same concept.
no code implementations • 16 Nov 2017 • Sungmin Rhee, Seokjun Seo, Sun Kim
In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN).
no code implementations • WS 2017 • Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu
We describe a Deep Learning approach to modeling the relevance of a document{'}s text to a query, applied to biomedical literature.
no code implementations • WS 2017 • Rezarta Islamaj Do{\u{g}}an, Andrew Chatr-aryamontri, Sun Kim, Chih-Hsuan Wei, Yifan Peng, Donald Comeau, Zhiyong Lu
The Precision Medicine Track in BioCre-ative VI aims to bring together the Bi-oNLP community for a novel challenge focused on mining the biomedical litera-ture in search of mutations and protein-protein interactions (PPI).
no code implementations • 5 Aug 2016 • Sun Kim, Nicolas Fiorini, W. John Wilbur, Zhiyong Lu
Here we present a query-document similarity measure motivated by the Word Mover's Distance.
no code implementations • Journal of Biomedical Informatics 2015 • Sun Kim, Haibin Liu, Lana Yeganova, W. John Wilbur
In this work, we propose an efficient and scalable system using a linear kernel to identify DDI information.