Search Results for author: Chun-Nan Hsu

Found 11 papers, 2 papers with code

Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction

1 code implementation AKBC 2021 William Hogan, Molly Huang, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, Yoshiki Vazquez Baeza, Andrew Bartko, Chun-Nan Hsu

In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types.

Relation Extraction

Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation

no code implementations Findings (EMNLP) 2021 An Yan, Zexue He, Xing Lu, Jiang Du, Eric Chang, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu

Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation.

Contrastive Learning Medical Report Generation +1

Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery

no code implementations12 Nov 2020 Canlin Zhang, Chun-Nan Hsu, Yannis Katsis, Ho-Cheol Kim, Yoshiki Vazquez-Baeza

Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics.

Knowledge Graph Completion Link Prediction +1

Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays

no code implementations Findings of the Association for Computational Linguistics 2020 Jianmo Ni, Chun-Nan Hsu, Amilcare Gentili, Julian McAuley

In this work, we focus on reporting abnormal findings on radiology images; instead of training on complete radiology reports, we propose a method to identify abnormal findings from the reports in addition to grouping them with unsupervised clustering and minimal rules.

Cross-Modal Retrieval Text Generation

Antibody Watch: Text Mining Antibody Specificity from the Literature

1 code implementation5 Aug 2020 Chun-Nan Hsu, Chia-Hui Chang, Thamolwan Poopradubsil, Amanda Lo, Karen A. William, Ko-Wei Lin, Anita Bandrowski, Ibrahim Burak Ozyurt, Jeffrey S. Grethe, Maryann E. Martone

Given an input article, the first task is to identify snippets about antibody specificity and classify if the snippets report that any antibody exhibits non-specificity, and thus is problematic.

NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction

no code implementations AKBC 2019 Dustin Wright, Yannis Katsis, Raghav Mehta, Chun-Nan Hsu

Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging.

Word Embeddings

The Impact of Automatic Pre-annotation in Clinical Note Data Element Extraction - the CLEAN Tool

no code implementations11 Aug 2018 Tsung-Ting Kuo, Jina Huh, Ji-Hoon Kim, Robert El-Kareh, Siddharth Singh, Stephanie Feudjio Feupe, Vincent Kuri, Gordon Lin, Michele E. Day, Lucila Ohno-Machado, Chun-Nan Hsu

Our study introduces CLEAN (CLinical note rEview and ANnotation), a pre-annotation-based cNLP annotation system to improve clinical note annotation of data elements, and comprehensively compares CLEAN with the widely-used annotation system Brat Rapid Annotation Tool (BRAT).

Natural Language Processing

Periodic Step Size Adaptation for Single Pass On-line Learning

no code implementations NeurIPS 2009 Chun-Nan Hsu, Yu-Ming Chang, Hanshen Huang, Yuh-Jye Lee

It has been established that the second-order stochastic gradient descent (2SGD) method can potentially achieve generalization performance as well as empirical optimum in a single pass (i. e., epoch) through the training examples.

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