Mor Geva
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Explainable Artificial Intelligence (XAI)
- Semantic Web and Ontologies
- Advanced Graph Neural Networks
- Text Readability and Simplification
-
- Multimodal Machine Learning Applications
Papers in
-
- Topic Modeling 25
- Natural Language Processing Techniques 21
- Text Readability and Simplification 4
- Explainable Artificial Intelligence (XAI) 3
- Speech and dialogue systems 2
-
- Multimodal Machine Learning Applications 5
- Co-authors
- Jonathan Berant (8 shared papers)Yoav Goldberg (4 shared papers)Amir Globerson (6 shared papers)Elad Segal (2 shared papers)Dan Roth (1 shared paper)Tushar Khot (1 shared paper)Daniel Khashabi (1 shared paper)Avi Caciularu (3 shared papers)
- Journals
- Transactions of the Association for Computational Linguistics (4 papers)Infoscience (Ecole Polytechnique Fédérale de Lausanne) (1 paper)
- Partner nations
- United StatesIsraelUnited Kingdom
In The Last Decade
Mor Geva
27 papers receiving 446 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 380
- Computer Vision and Pattern Recognition 98
- Health Informatics 6
- General Social Sciences 11
- Software 11
Countries citing papers authored by Mor Geva
This map shows the geographic impact of Mor Geva'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 Mor Geva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mor Geva more than expected).
Fields of papers citing papers by Mor Geva
This network shows the impact of papers produced by Mor Geva. 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 Mor Geva. The network helps show where Mor Geva may publish in the future.
Co-authors
The 25 scholars most cited alongside Mor Geva, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 111 | |
| 2 | 2020 | 64 | |
| 3 | 2022 | 62 | |
| 4 | 2023 | 30 | |
| 5 | 2022 | 28 | |
| 6 | 2023 | 25 | |
| 7 | 2023 | 22 | |
| 8 | 2023 | 19 | |
| 9 | 2023 | 18 | |
| 10 | 2019 | 16 | |
| 11 | 2023 | 11 | |
| 12 | 2024 | 11 | |
| 13 | 2022 | 9 | |
| 14 | 2023 | 6 | |
| 15 | 2022 | 5 | |
| 16 | 2022 | 5 | |
| 17 | 2024 | 4 | |
| 18 | 2023 | 3 | |
| 19 | 2024 | 3 | |
| 20 | 2023 | 3 |
About Mor Geva
Mor Geva is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Cognitive Neuroscience and General Social Sciences, having authored 31 papers that have together received 464 indexed citations. Recurring topics across this work include Topic Modeling (25 papers), Natural Language Processing Techniques (21 papers), Multimodal Machine Learning Applications (5 papers), Text Readability and Simplification (4 papers), Software Engineering Research (3 papers), Explainable Artificial Intelligence (XAI) (3 papers), Speech and dialogue systems (2 papers) and Computational and Text Analysis Methods (1 paper). The work is most often cited by research in Artificial Intelligence (380 citations), Computer Vision and Pattern Recognition (98 citations), Health Informatics (6 citations), General Social Sciences (11 citations) and Software (11 citations). Mor Geva has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Jonathan Berant, Yoav Goldberg, Amir Globerson, Elad Segal, Dan Roth, Tushar Khot, Daniel Khashabi, Avi Caciularu, Kevin I‐Kai Wang and Matt Gardner. Their work appears in journals such as Transactions of the Association for Computational Linguistics and Infoscience (Ecole Polytechnique Fédérale de Lausanne).
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.