Jonathan Mallinson
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems
- Molecular Biology
- Computer Networks and Communications
- Co-authors
- Mirella LapataRico SennrichSiva ReddyLi DongEric MalmiJakub AdámekAliaksei SeverynAleksandr Chuklin
- Topics
- Natural Language Processing Techniques (9 papers)Topic Modeling (8 papers)Text Readability and Simplification (4 papers)
- Journals
- The Modern Language ReviewEdinburgh Research Explorer (University of Edinburgh)Zurich Open Repository and Archive (University of Zurich)
- Partner nations
- United KingdomUnited StatesSwitzerland
In The Last Decade
Jonathan Mallinson
9 papers receiving 286 citations
Peers
Comparison fields: 5 of 28
- Artificial Intelligence 309
- Computer Vision and Pattern Recognition 90
- Information Systems 34
- Molecular Biology 7
- Computer Networks and Communications 3
Countries citing papers authored by Jonathan Mallinson
This map shows the geographic impact of Jonathan Mallinson'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 Jonathan Mallinson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Mallinson more than expected).
Fields of papers citing papers by Jonathan Mallinson
This network shows the impact of papers produced by Jonathan Mallinson. 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 Jonathan Mallinson. The network helps show where Jonathan Mallinson may publish in the future.
Co-authorship network of co-authors of Jonathan Mallinson
This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan Mallinson. A scholar is included among the top collaborators of Jonathan Mallinson 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 Jonathan Mallinson. Jonathan Mallinson 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 | 48 | |
| 3 | 0 | |
| 4 | 19 | |
| 5 | 17 | |
| 6 | 12 | |
| 7 | 1 | |
| 8 | 7 | |
| 9 | 110 | |
| 10 | 105 | |
| 11 | 0 | |
| 12 | 1 |
About Jonathan Mallinson
Jonathan Mallinson is a scholar working on Classics, Artificial Intelligence and Anthropology, having authored 12 papers that have together received 321 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (9 papers), Topic Modeling (8 papers) and Text Readability and Simplification (4 papers). The work is most often cited by research in Artificial Intelligence (309 citations), Computer Vision and Pattern Recognition (90 citations) and Information Systems (34 citations). Jonathan Mallinson has collaborated with scholars based in United Kingdom, United States and Switzerland. Frequent co-authors include Mirella Lapata, Rico Sennrich, Siva Reddy, Li Dong, Eric Malmi, Jakub Adámek, Aliaksei Severyn, Aleksandr Chuklin, Yue Dong and Felix Stahlberg. Their work appears in journals such as The Modern Language Review, Edinburgh Research Explorer (University of Edinburgh) and Zurich Open Repository and Archive (University of Zurich).
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.