Papers by David Kartchner
A Comprehensive Evaluation of Biomedical Entity Linking Models (2023.emnlp-main)
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David Kartchner, Jennifer Deng, Shubham Lohiya, Tejasri Kopparthi, Prasanth Bathala, Daniel Domingo-Fernández, Cassie Mitchell
| Challenge: | Current methods struggle to correctly link genes and proteins and often have difficulty incorporating context into linking decisions. |
| Approach: | They evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. |
| Outcome: | The proposed models are compared along axes of accuracy, speed, ease of use, generalization, adaptability and adaptability to new ontologies and datasets. |
BioEL: A Comprehensive Python Package for Biomedical Entity Linking (2025.findings-naacl)
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Prasanth Bathala, Christophe Ye, Batuhan Nursal, Shubham Lohiya, David Kartchner, Cassie S. Mitchell
| Challenge: | Entity Linking in biomedical literature is a critical task that enhances the extraction and integration of information from diverse scientific literature. |
| Approach: | They propose a Python package that allows for better Entity Linking in biomedical literature . the package includes four components: Ontology Object, Dataset Object and Evaluation Framework . |
| Outcome: | The proposed open-source package enables the implementation and comparison of biomedical entity linking tasks. |
Denoising Multi-Source Weak Supervision for Neural Text Classification (2020.findings-emnlp)
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| Challenge: | Recent years have witnessed the rapid development of deep neural networks (DNNs) for text classification problems. |
| Approach: | They propose a label denoiser which estimates the source reliability using a conditional soft attention mechanism and reduces label noise by aggregating rule-annotated weak labels. |
| Outcome: | The proposed model outperforms state-of-the-art methods on sentiment, topic, and relation classifications and achieves comparable performance with fully-supervised methods even without labeled data. |