Ivan Sekulić
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Sentiment Analysis and Opinion Mining
- Speech and dialogue systems
- Advanced Text Analysis Techniques
- Natural Language Processing Techniques
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- Recommender Systems and Techniques
Papers in
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- Topic Modeling 8
- Speech and dialogue systems 7
- Natural Language Processing Techniques 5
- Advanced Text Analysis Techniques 3
- Sentiment Analysis and Opinion Mining 1
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- Service-Oriented Architecture and Web Services 1
- Co-authors
- Fábio Crestani (7 shared papers)Mohammad Aliannejadi (5 shared papers)Jan Šnajder (2 shared papers)Mladen Karan (1 shared paper)Jeff Dalton (1 shared paper)Jiale Han (1 shared paper)Bo Cheng (1 shared paper)Hao Yang (1 shared paper)
- Journals
- ACM Transactions on Intelligent Systems and Technology (1 paper)arXiv (Cornell University) (1 paper)Text REtrieval Conference (2 papers)UvA-DARE (University of Amsterdam) (2 papers)
- Partner nations
- SwitzerlandNetherlandsCroatia
In The Last Decade
Ivan Sekulić
8 papers receiving 142 citations
Peers
Comparison fields: 5 of 29
- Artificial Intelligence 114
- Information Systems 42
- Applied Psychology 6
- Social Psychology 25
- Statistical and Nonlinear Physics 11
Countries citing papers authored by Ivan Sekulić
This map shows the geographic impact of Ivan Sekulić'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 Ivan Sekulić with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Sekulić more than expected).
Fields of papers citing papers by Ivan Sekulić
This network shows the impact of papers produced by Ivan Sekulić. 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 Ivan Sekulić. The network helps show where Ivan Sekulić may publish in the future.
Co-authors
The 13 scholars most cited alongside Ivan Sekulić, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 38 | |
| 2 | 2018 | 33 | |
| 3 | 2022 | 31 | |
| 4 | 2021 | 20 | |
| 5 | 2023 | 16 | |
| 6 | 2024 | 7 | |
| 7 | Extending the Use of Previous Relevant Utterances for Response Ranking in Conversational Search. | 2020 | 3 |
| 8 | Longformer for MS MARCO Document Re-ranking Task | 2020 | 1 |
| 9 | 2021 | 0 | |
| 10 | 2025 | 0 | |
| 11 | 2024 | 0 |
About Ivan Sekulić
Ivan Sekulić is a scholar working on Artificial Intelligence, Information Systems, Social Psychology, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 149 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Speech and dialogue systems (7 papers), Natural Language Processing Techniques (5 papers), Advanced Text Analysis Techniques (3 papers), Digital Mental Health Interventions (1 paper), Service-Oriented Architecture and Web Services (1 paper), Mental Health via Writing (1 paper) and Sentiment Analysis and Opinion Mining (1 paper). The work is most often cited by research in Artificial Intelligence (114 citations), Information Systems (42 citations), Applied Psychology (6 citations), Social Psychology (25 citations) and Statistical and Nonlinear Physics (11 citations). Ivan Sekulić has collaborated with scholars based in Switzerland, Netherlands and Croatia. Frequent co-authors include Fábio Crestani, Mohammad Aliannejadi, Jan Šnajder, Mladen Karan, Jeff Dalton, Jiale Han, Bo Cheng, Hao Yang, Guoshun Nan and Paolo Rosso. Their work appears in journals such as ACM Transactions on Intelligent Systems and Technology, arXiv (Cornell University), Text REtrieval Conference and UvA-DARE (University of Amsterdam).
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.