Eunsol Choi
- Artificial Intelligence top 0.5%
- Sociology and Political Science top 5%
- Information Systems top 2%
- Computer Vision and Pattern Recognition top 2%
- Signal Processing top 10%
- Co-authors
- Yejin ChoiLuke ZettlemoyerHannah RashkinJin Yea JangSvitlana VolkovaTom KwiatkowskiMohit IyyerMark Yatskar
- Topics
- Topic Modeling (35 papers)Natural Language Processing Techniques (33 papers)Multimodal Machine Learning Applications (13 papers)
- Journals
- Language Resources and EvaluationTransactions of the Association for Computational LinguisticsProceedings of the ACM on Human-Computer Interaction
- Partner nations
- United StatesJapanIsrael
In The Last Decade
Eunsol Choi
42 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 1.7k
- Sociology and Political Science 475
- Information Systems 427
- Computer Vision and Pattern Recognition 406
- Signal Processing 78
Countries citing papers authored by Eunsol Choi
This map shows the geographic impact of Eunsol Choi'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 Eunsol Choi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eunsol Choi more than expected).
Fields of papers citing papers by Eunsol Choi
This network shows the impact of papers produced by Eunsol Choi. 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 Eunsol Choi. The network helps show where Eunsol Choi may publish in the future.
Co-authorship network of co-authors of Eunsol Choi
This figure shows the co-authorship network connecting the top 25 collaborators of Eunsol Choi. A scholar is included among the top collaborators of Eunsol Choi 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 Eunsol Choi. Eunsol Choi 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 | 0 | |
| 5 | 13 | |
| 6 | 2 | |
| 7 | 4 | |
| 8 | 18 | |
| 9 | 17 | |
| 10 | 5 | |
| 11 | 16 | |
| 12 | 8 | |
| 13 | 12 | |
| 14 | 6 | |
| 15 | 48 | |
| 16 | 111 | |
| 17 | QuAC: Question Answering in Contextbreakdown → | 343 |
| 18 | Truth of Varying Shades: On Political Fact-Checking and Fake News | 1 |
| 19 | 79 | |
| 20 | Extracting Structured Scholarly Information from the Machine Translation Literature | 3 |
About Eunsol Choi
Eunsol Choi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 48 papers that have together received 1.9k indexed citations. Recurring topics across this work include Topic Modeling (35 papers), Natural Language Processing Techniques (33 papers) and Multimodal Machine Learning Applications (13 papers). The work is most often cited by research in Artificial Intelligence (1.7k citations), Computer Vision and Pattern Recognition (406 citations) and Information Systems (427 citations). Eunsol Choi has collaborated with scholars based in United States, Japan and Israel. Frequent co-authors include Yejin Choi, Luke Zettlemoyer, Hannah Rashkin, Jin Yea Jang, Svitlana Volkova, Tom Kwiatkowski, Mohit Iyyer, Mark Yatskar, Wen-tau Yih and He He. Their work appears in journals such as Language Resources and Evaluation, Transactions of the Association for Computational Linguistics and Proceedings of the ACM on Human-Computer Interaction.
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.