Nikola Mrkšić
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Social Psychology
- Signal Processing
- Co-authors
- Milica GašićSteve YoungPei-Hao SuTsung-Hsien WenDavid VandykeStefan UltesLina M. Rojas BarahonaIvan Vulić
- Topics
- Speech and dialogue systems (16 papers)Topic Modeling (16 papers)Natural Language Processing Techniques (9 papers)
- Journals
- Computer Speech & LanguageTransactions of the Association for Computational LinguisticsApollo (University of Cambridge)
- Partner nations
- United KingdomGermanyIsrael
In The Last Decade
Nikola Mrkšić
17 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 1.3k
- Computer Vision and Pattern Recognition 237
- Information Systems 90
- Social Psychology 37
- Signal Processing 24
Countries citing papers authored by Nikola Mrkšić
This map shows the geographic impact of Nikola Mrkšić'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 Nikola Mrkšić with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nikola Mrkšić more than expected).
Fields of papers citing papers by Nikola Mrkšić
This network shows the impact of papers produced by Nikola Mrkšić. 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 Nikola Mrkšić. The network helps show where Nikola Mrkšić may publish in the future.
Co-authorship network of co-authors of Nikola Mrkšić
This figure shows the co-authorship network connecting the top 25 collaborators of Nikola Mrkšić. A scholar is included among the top collaborators of Nikola Mrkšić 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 Nikola Mrkšić. Nikola Mrkšić is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 19 | |
| 2 | 8 | |
| 3 | 43 | |
| 4 | 9 | |
| 5 | 75 | |
| 6 | A Network-based End-to-End Trainable Task-oriented Dialogue Systembreakdown → | 451 |
| 7 | 16 | |
| 8 | 105 | |
| 9 | Exploiting sentence and context representations in deep neural models for spoken language understanding | 7 |
| 10 | 64 | |
| 11 | 18 | |
| 12 | 37 | |
| 13 | Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systemsbreakdown → | 466 |
| 14 | 13 | |
| 15 | 14 | |
| 16 | 19 | |
| 17 | 24 |
About Nikola Mrkšić
Nikola Mrkšić is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Social Psychology, having authored 17 papers that have together received 1.4k indexed citations. Recurring topics across this work include Speech and dialogue systems (16 papers), Topic Modeling (16 papers) and Natural Language Processing Techniques (9 papers). The work is most often cited by research in Artificial Intelligence (1.3k citations), Computer Vision and Pattern Recognition (237 citations) and Information Systems (90 citations). Nikola Mrkšić has collaborated with scholars based in United Kingdom, Germany and Israel. Frequent co-authors include Milica Gašić, Steve Young, Pei-Hao Su, Tsung-Hsien Wen, David Vandyke, Stefan Ultes, Lina M. Rojas Barahona, Ivan Vulić, Iñigo Casanueva and Paweł Budzianowski. Their work appears in journals such as Computer Speech & Language, Transactions of the Association for Computational Linguistics and Apollo (University of Cambridge).
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.