Phu Mon Htut
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems
- General Social Sciences top 10%
- Health Informatics
- Co-authors
- Samuel BowmanJason PhangSamuel R. BowmanHaokun LiuKyunghyun ChoAlicia ParrishRichard Yuanzhe PangXiaoyi Zhang
- Topics
- Topic Modeling (8 papers)Natural Language Processing Techniques (7 papers)Multimodal Machine Learning Applications (3 papers)
- Journals
- University of Groningen research database (University of Groningen / Centre for Information Technology)OpenBU (Boston University)Findings of the Association for Computational Linguistics: ACL 2022
- Partner nations
- United StatesNetherlandsCanada
In The Last Decade
Phu Mon Htut
9 papers receiving 235 citations
Peers
Comparison fields: 5 of 44
- Artificial Intelligence 217
- Computer Vision and Pattern Recognition 60
- Information Systems 24
- General Social Sciences 10
- Health Informatics 9
Countries citing papers authored by Phu Mon Htut
This map shows the geographic impact of Phu Mon Htut'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 Phu Mon Htut with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Phu Mon Htut more than expected).
Fields of papers citing papers by Phu Mon Htut
This network shows the impact of papers produced by Phu Mon Htut. 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 Phu Mon Htut. The network helps show where Phu Mon Htut may publish in the future.
Co-authorship network of co-authors of Phu Mon Htut
This figure shows the co-authorship network connecting the top 25 collaborators of Phu Mon Htut. A scholar is included among the top collaborators of Phu Mon Htut 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 Phu Mon Htut. Phu Mon Htut 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 | 2 | |
| 3 | 1 | |
| 4 | 64 | |
| 5 | 1 | |
| 6 | 88 | |
| 7 | 48 | |
| 8 | 20 | |
| 9 | 20 |
About Phu Mon Htut
Phu Mon Htut is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 9 papers that have together received 245 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Natural Language Processing Techniques (7 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Artificial Intelligence (217 citations), Health Informatics (9 citations) and General Social Sciences (10 citations). Phu Mon Htut has collaborated with scholars based in United States, Netherlands and Canada. Frequent co-authors include Samuel Bowman, Jason Phang, Samuel R. Bowman, Haokun Liu, Kyunghyun Cho, Alicia Parrish, Richard Yuanzhe Pang, Xiaoyi Zhang, Katharina Kann and Clara Vania. Their work appears in journals such as University of Groningen research database (University of Groningen / Centre for Information Technology), OpenBU (Boston University) and Findings of the Association for Computational Linguistics: ACL 2022.
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.