A system for coreference resolution for the clinical narrative
By Jiaping Zheng, Wendy W. Chapman, Timothy Miller, Chen Lin, R. Crowley, Guergana K. Savova. 2012
41 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dmitriy Dligach
- Tristan Naumann
- Zexian Zeng
- Xiaoyu Li
- Yu Deng
- Timothy Miller
- Yuan Luo
- Guergana K. Savova
In Molecular Biology
- Zexian Zeng
- Tristan Naumann
- Xiaoyu Li
- Yu Deng
- Timothy Miller
- Yuan Luo
- Dmitriy Dligach
- Guergana K. Savova
Citations in the corpus
- Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[129] found that a SVM with RBF kernel exceeded non-kernel-based SVMs, decision trees, and perceptron for coreference resolution identification from the clinical narrative.”
- Active Learning for Coreference Resolution (2012, methodology): “The system used here (Zheng et al., 2012) is an end-to-end coreference resolution system, meaning that the algorithm receives no gold standard information about mentions, named entity types, or any linguistic information.”