Tagyoung Chung
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Molecular Biology
- Information Systems
- Social Psychology
- Co-authors
- Daniel GildeaShuyang GaoDi JinDilek Hakkani‐TürSanchit AgarwalMatt PostThomas KollarMichel Galley
- Topics
- Topic Modeling (15 papers)Natural Language Processing Techniques (15 papers)Speech and dialogue systems (5 papers)
- Journals
- Computational LinguisticsEmpirical Methods in Natural Language ProcessingNorth American Chapter of the Association for Computational Linguistics
- Partner nations
- United StatesFrance
In The Last Decade
Tagyoung Chung
20 papers receiving 215 citations
Peers
Comparison fields: 5 of 29
- Artificial Intelligence 221
- Computer Vision and Pattern Recognition 38
- Molecular Biology 15
- Information Systems 11
- Social Psychology 6
Countries citing papers authored by Tagyoung Chung
This map shows the geographic impact of Tagyoung Chung'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 Tagyoung Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tagyoung Chung more than expected).
Fields of papers citing papers by Tagyoung Chung
This network shows the impact of papers produced by Tagyoung Chung. 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 Tagyoung Chung. The network helps show where Tagyoung Chung may publish in the future.
Co-authorship network of co-authors of Tagyoung Chung
This figure shows the co-authorship network connecting the top 25 collaborators of Tagyoung Chung. A scholar is included among the top collaborators of Tagyoung Chung based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tagyoung Chung. Tagyoung Chung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 5 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 6 | |
| 8 | 4 | |
| 9 | 0 | |
| 10 | 33 | |
| 11 | 31 | |
| 12 | Tuning as Linear Regression | 8 |
| 13 | Direct Error Rate Minimization for Statistical Machine Translation | 5 |
| 14 | Issues Concerning Decoding with Synchronous Context-free Grammar | 10 |
| 15 | Terminal-Aware Synchronous Binarization | 3 |
| 16 | 3 | |
| 17 | Factors Affecting the Accuracy of Korean Parsing | 15 |
| 18 | 34 | |
| 19 | 38 | |
| 20 | Identifying technical vocabulary, System | 2 |
About Tagyoung Chung
Tagyoung Chung is a scholar working on Artificial Intelligence, General Social Sciences and Computer Vision and Pattern Recognition, having authored 23 papers that have together received 237 indexed citations. Recurring topics across this work include Topic Modeling (15 papers), Natural Language Processing Techniques (15 papers) and Speech and dialogue systems (5 papers). The work is most often cited by research in Artificial Intelligence (221 citations), Computer Vision and Pattern Recognition (38 citations) and Human-Computer Interaction (3 citations). Tagyoung Chung has collaborated with scholars based in United States and France. Frequent co-authors include Daniel Gildea, Shuyang Gao, Di Jin, Dilek Hakkani‐Tür, Sanchit Agarwal, Matt Post, Thomas Kollar, Michel Galley, Emma Strubell and Spyros Matsoukas. Their work appears in journals such as Computational Linguistics, Empirical Methods in Natural Language Processing and North American Chapter of the Association for Computational Linguistics.
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.