John Niekrasz
- Artificial Intelligence top 10%
- Social Psychology
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Information Systems
- Co-authors
- Matthew PurverStanley PetersAlexander GruensteinJohanna D. MoorePatrick EhlenJohn DowdingSharareh NoorbaloochiDan Jurafsky
- Topics
- Speech and dialogue systems (13 papers)Natural Language Processing Techniques (13 papers)Topic Modeling (10 papers)
- Journals
- Planta MedicaLanguage Resources and EvaluationEdinburgh Research Explorer
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
John Niekrasz
21 papers receiving 178 citations
Peers
Comparison fields: 5 of 34
- Artificial Intelligence 178
- Social Psychology 25
- Human-Computer Interaction 22
- Computer Vision and Pattern Recognition 17
- Information Systems 14
Countries citing papers authored by John Niekrasz
This map shows the geographic impact of John Niekrasz'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 John Niekrasz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Niekrasz more than expected).
Fields of papers citing papers by John Niekrasz
This network shows the impact of papers produced by John Niekrasz. 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 John Niekrasz. The network helps show where John Niekrasz may publish in the future.
Co-authorship network of co-authors of John Niekrasz
This figure shows the co-authorship network connecting the top 25 collaborators of John Niekrasz. A scholar is included among the top collaborators of John Niekrasz 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 John Niekrasz. John Niekrasz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 10 | |
| 4 | 1 | |
| 5 | Annotating Participant Reference in English Spoken Conversation | 1 |
| 6 | 4 | |
| 7 | Proceedings of the SIGDIAL 2009 Conference | 14 |
| 8 | 16 | |
| 9 | 35 | |
| 10 | A Meeting Browser that Learns. | 7 |
| 11 | 18 | |
| 12 | NOMOS: A Semantic Web Software Framework for Annotation of Multimodal Corpora. | 7 |
| 13 | 5 | |
| 14 | Ontology-Based Discourse Understanding for a Persistent Meeting Assistant. | 18 |
| 15 | A Multimodal Discourse Ontology for Meeting Understanding | 1 |
| 16 | 3 | |
| 17 | 24 | |
| 18 | 2 | |
| 19 | 13 | |
| 20 | 23 |
About John Niekrasz
John Niekrasz is a scholar working on Artificial Intelligence, Computer Science Applications and Human-Computer Interaction, having authored 21 papers that have together received 214 indexed citations. Recurring topics across this work include Speech and dialogue systems (13 papers), Natural Language Processing Techniques (13 papers) and Topic Modeling (10 papers). The work is most often cited by research in Artificial Intelligence (178 citations), Human-Computer Interaction (22 citations) and Computer Science Applications (14 citations). John Niekrasz has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Matthew Purver, Stanley Peters, Alexander Gruenstein, Johanna D. Moore, Patrick Ehlen, John Dowding, Sharareh Noorbaloochi, Dan Jurafsky, Ed Kaiser and Xiaoguang Li. Their work appears in journals such as Planta Medica, Language Resources and Evaluation and Edinburgh Research Explorer.
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.