Iulia Turc
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Speech Recognition and Synthesis
- Text Readability and Simplification
Papers in
-
- Natural Language Processing Techniques 4
- Topic Modeling 4
- Explainable Artificial Intelligence (XAI) 1
- Text Readability and Simplification 1
-
- Multimodal Machine Learning Applications 2
- Co-authors
- Kenton Lee (2 shared papers)Ming‐Wei Chang (1 shared paper)Kristina Toutanova (1 shared paper)John Wieting (1 shared paper)Jonathan H. Clark (1 shared paper)Dan Garrette (1 shared paper)Matthew S. Lamm (2 shared papers)Slav Petrov (2 shared papers)
- Journals
- Computational Linguistics (2 papers)Transactions of the Association for Computational Linguistics (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Iulia Turc
5 papers receiving 150 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 139
- Health Informatics 3
- Computer Vision and Pattern Recognition 35
- Management Science and Operations Research 12
- Information Systems 21
Countries citing papers authored by Iulia Turc
This map shows the geographic impact of Iulia Turc's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Iulia Turc with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Iulia Turc more than expected).
Fields of papers citing papers by Iulia Turc
This network shows the impact of papers produced by Iulia Turc. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Iulia Turc. The network helps show where Iulia Turc may publish in the future.
Co-authors
The 17 scholars most cited alongside Iulia Turc, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation | 2019 | 72 |
| 2 | 2022 | 57 | |
| 3 | 2023 | 20 | |
| 4 | 2023 | 7 | |
| 5 | 2020 | 2 |
About Iulia Turc
Iulia Turc is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Education, Infectious Diseases and Organic Chemistry, having authored 5 papers that have together received 158 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (4 papers), Topic Modeling (4 papers), Multimodal Machine Learning Applications (2 papers), Explainable Artificial Intelligence (XAI) (1 paper), Text Readability and Simplification (1 paper) and Education and Critical Thinking Development (1 paper). The work is most often cited by research in Artificial Intelligence (139 citations), Health Informatics (3 citations), Computer Vision and Pattern Recognition (35 citations), Management Science and Operations Research (12 citations) and Information Systems (21 citations). Iulia Turc has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Kenton Lee, Ming‐Wei Chang, Kristina Toutanova, John Wieting, Jonathan H. Clark, Dan Garrette, Matthew S. Lamm, Slav Petrov, David Reitter and Hannah Rashkin. Their work appears in journals such as Computational Linguistics, Transactions of the Association for Computational Linguistics and arXiv (Cornell University).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.