Potsawee Manakul
- Artificial Intelligence top 10%
- Information Systems
- Health Informatics top 10%
- Computer Vision and Pattern Recognition
- Experimental and Cognitive Psychology
- Topics
- Natural Language Processing Techniques (9 papers)Topic Modeling (9 papers)Text Readability and Simplification (4 papers)
- Journals
- Apollo (University of Cambridge)arXiv (Cornell University)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Partner nations
- United KingdomSouth Sudan
In The Last Decade
Potsawee Manakul
12 papers receiving 164 citations
Hit Papers
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 130
- Information Systems 17
- Health Informatics 16
- Computer Vision and Pattern Recognition 9
- Experimental and Cognitive Psychology 7
Countries citing papers authored by Potsawee Manakul
This map shows the geographic impact of Potsawee Manakul'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 Potsawee Manakul with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Potsawee Manakul more than expected).
Fields of papers citing papers by Potsawee Manakul
This network shows the impact of papers produced by Potsawee Manakul. 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 Potsawee Manakul. The network helps show where Potsawee Manakul may publish in the future.
Co-authorship network of co-authors of Potsawee Manakul
This figure shows the co-authorship network connecting the top 25 collaborators of Potsawee Manakul. A scholar is included among the top collaborators of Potsawee Manakul 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 Potsawee Manakul. Potsawee Manakul 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 | 4 | |
| 3 | 4 | |
| 4 | 4 | |
| 5 | SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelsbreakdown → | 126 |
| 6 | 1 | |
| 7 | Long-Span Dependencies in Transformer-based Summarization Systems. | 1 |
| 8 | 2 | |
| 9 | 8 | |
| 10 | 5 | |
| 11 | 13 | |
| 12 | 4 | |
| 13 | 1 |
About Potsawee Manakul
Potsawee Manakul is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Information Systems, having authored 13 papers that have together received 173 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (9 papers), Topic Modeling (9 papers) and Text Readability and Simplification (4 papers). The work is most often cited by research in Health Informatics (16 citations), Artificial Intelligence (130 citations) and Information Systems and Management (7 citations). Potsawee Manakul has collaborated with scholars based in United Kingdom and South Sudan. Frequent co-authors include Mark Gales, K.M. Knill, Andrew Caines, Linlin Wang, Yu Wang, Yizhou Wang, Kate Knill, Yijuan Lu, Guangzhi Sun and Philip C. Woodland. Their work appears in journals such as Apollo (University of Cambridge), arXiv (Cornell University) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.