Jena D. Hwang
- Artificial Intelligence top 2%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Language and Linguistics top 10%
- Information Systems
- Co-authors
- Martha PalmerYejin ChoiRonan Le BrasClaire BonialJinho D. ChoiChandra BhagavatulaMaxwell ForbesXiming Lu
- Topics
- Natural Language Processing Techniques (38 papers)Topic Modeling (35 papers)Speech and dialogue systems (11 papers)
- Journals
- Journal of the American Medical Informatics AssociationComputational LinguisticsNature Machine Intelligence
- Partner nations
- United StatesUnited KingdomJapan
In The Last Decade
Jena D. Hwang
47 papers receiving 613 citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 580
- Molecular Biology 106
- Computer Vision and Pattern Recognition 65
- Language and Linguistics 44
- Information Systems 38
Countries citing papers authored by Jena D. Hwang
This map shows the geographic impact of Jena D. Hwang'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 Jena D. Hwang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jena D. Hwang more than expected).
Fields of papers citing papers by Jena D. Hwang
This network shows the impact of papers produced by Jena D. Hwang. 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 Jena D. Hwang. The network helps show where Jena D. Hwang may publish in the future.
Co-authorship network of co-authors of Jena D. Hwang
This figure shows the co-authorship network connecting the top 25 collaborators of Jena D. Hwang. A scholar is included among the top collaborators of Jena D. Hwang 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 Jena D. Hwang. Jena D. Hwang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 5 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 16 | |
| 6 | 8 | |
| 7 | 9 | |
| 8 | 5 | |
| 9 | 11 | |
| 10 | 98 | |
| 11 | 5 | |
| 12 | 55 | |
| 13 | 5 | |
| 14 | Building Universal Dependency Treebanks in Korean | 17 |
| 15 | Improving DISCERN with Deep Learning. | 2 |
| 16 | Event Nugget Detection and Argument Extraction with DISCERN | 1 |
| 17 | Criteria for Identifying and Annotating Caused Motion Constructions in Corpus Data | 3 |
| 18 | 103 | |
| 19 | Identifying Assertions in Text and Discourse: The Presentational Relative Clause Construction | 4 |
| 20 | Leveraging Lexical Resources for the Detection of Event Relations. | 4 |
About Jena D. Hwang
Jena D. Hwang is a scholar working on Artificial Intelligence, Language and Linguistics and Safety Research, having authored 48 papers that have together received 676 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (38 papers), Topic Modeling (35 papers) and Speech and dialogue systems (11 papers). The work is most often cited by research in Artificial Intelligence (580 citations), Health Informatics (9 citations) and General Social Sciences (17 citations). Jena D. Hwang has collaborated with scholars based in United States, United Kingdom and Japan. Frequent co-authors include Martha Palmer, Yejin Choi, Ronan Le Bras, Claire Bonial, Jinho D. Choi, Chandra Bhagavatula, Maxwell Forbes, Ximing Lu, Jack Hessel and Liwei Jiang. Their work appears in journals such as Journal of the American Medical Informatics Association, Computational Linguistics and Nature Machine Intelligence.
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.