Dimitri Kartsaklis
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Molecular Biology
- Information Systems
- Co-authors
- Mehrnoosh SadrzadehStephen PulmanJianpeng ChengMatthew PurverMohammad Taher PilehvarNigel CollierKonstantinos MeichanetzidisBob Coecke
- Topics
- Topic Modeling (13 papers)Natural Language Processing Techniques (12 papers)Speech and dialogue systems (2 papers)
- Journals
- SHILAP Revista de lepidopterologíaJournal of Artificial Intelligence ResearchQuantum Machine Intelligence
- Partner nations
- United KingdomUnited States
In The Last Decade
Dimitri Kartsaklis
15 papers receiving 298 citations
Peers
Comparison fields: 5 of 34
- Artificial Intelligence 314
- Computer Vision and Pattern Recognition 33
- Computational Theory and Mathematics 20
- Molecular Biology 15
- Information Systems 14
Countries citing papers authored by Dimitri Kartsaklis
This map shows the geographic impact of Dimitri Kartsaklis'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 Dimitri Kartsaklis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dimitri Kartsaklis more than expected).
Fields of papers citing papers by Dimitri Kartsaklis
This network shows the impact of papers produced by Dimitri Kartsaklis. 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 Dimitri Kartsaklis. The network helps show where Dimitri Kartsaklis may publish in the future.
Co-authorship network of co-authors of Dimitri Kartsaklis
This figure shows the co-authorship network connecting the top 25 collaborators of Dimitri Kartsaklis. A scholar is included among the top collaborators of Dimitri Kartsaklis 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 Dimitri Kartsaklis. Dimitri Kartsaklis 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 | 41 | |
| 3 | 24 | |
| 4 | Sentence entailment in compositional distributional semantics | 8 |
| 5 | 28 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | 4 | |
| 9 | 30 | |
| 10 | 11 | |
| 11 | 53 | |
| 12 | 28 | |
| 13 | 41 | |
| 14 | Separating Disambiguation from Composition in Distributional Semantics | 25 |
| 15 | A Unified Sentence Space for Categorical Distributional-Compositional Semantics: Theory and Experiments | 33 |
About Dimitri Kartsaklis
Dimitri Kartsaklis is a scholar working on Computational Mathematics, Artificial Intelligence and General Social Sciences, having authored 15 papers that have together received 331 indexed citations. Recurring topics across this work include Topic Modeling (13 papers), Natural Language Processing Techniques (12 papers) and Speech and dialogue systems (2 papers). The work is most often cited by research in Computational Mathematics (7 citations), Artificial Intelligence (314 citations) and Computer Vision and Pattern Recognition (33 citations). Dimitri Kartsaklis has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Mehrnoosh Sadrzadeh, Stephen Pulman, Jianpeng Cheng, Matthew Purver, Mohammad Taher Pilehvar, Nigel Collier, Konstantinos Meichanetzidis, Bob Coecke, Nal Kalchbrenner and J. D. Williams. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of Artificial Intelligence Research and Quantum 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.