Ozlem Uzuner

Artificial Intelligence · 2010–2018

A researcher in the Science corpus: 11 papers; trusts 99 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 142 citations into it; #7,386 of 35,345 by trust here, #5,028 by citations
• Molecular Biology — 132 citations into it; #2,203 of 17,773 by trust here, #4,000 by citations
• Natural Language Processing — 47 citations into it
• Computational Theory and Mathematics — 22 citations into it
• Genetics — 21 citations into it
• Information Systems — 17 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it; #19,375 of 21,575 by trust here, #15,955 by citations
• Evaluation & Benchmarks — 14 citations into it

Whom Ozlem Uzuner trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alan R. Aronson
2. Olivier Bodenreider
3. Yuan Luo
4. Ralph Grishman
5. Michael Collins
6. Burr Settles
7. Yoav Freund
8. Thomas C. Rindflesch
9. Eugene Charniak
10. George A. Miller

Who trusts Ozlem Uzuner overall

1. Yuan Luo
2. Anusri Pampari
3. Dianbo Liu
4. Travis R. Goodwin
5. Shaodian Zhang
6. Xiang Dai
7. Sumithra Velupillai
8. Sanda M. Harabagiu
9. Aurélie Névéol
10. Preethi Raghavan

Papers from Ozlem Uzuner's perspective

Ordered by how strongly the researchers Ozlem Uzuner trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. ImageNet: A large-scale hierarchical image database
10. Dropout: a simple way to prevent neural networks from overfitting

Their papers

• Extracting medication information from clinical text (2010) — 515 citations
• De-identification of patient notes with recurrent neural networks (2016) — 433 citations
• 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text (2011) — 1,316 citations
• Evaluating temporal relations in clinical text: 2012 i2b2 Challenge (2013) — 509 citations
• Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017) — 75 citations
• Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes (2018) — 69 citations
• Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions (2011) — 305 citations
• Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text (2015) — 56 citations
• Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015)
• A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases (2015) — 24 citations
• Semi-Supervised Learning to Identify UMLS Semantic Relations (2014) — 9 citations