Peter Makarov
Impact in
- Artificial Intelligence top 10%
- Natural Language Processing Techniques
- Topic Modeling
- Speech Recognition and Synthesis
- Authorship Attribution and Profiling
- Speech and dialogue systems
- Sentiment Analysis and Opinion Mining
- General Social Sciences top 10%
- Computational and Text Analysis Methods
Papers in
-
- Natural Language Processing Techniques 11
- Topic Modeling 10
- Speech Recognition and Synthesis 4
- Speech and dialogue systems 2
-
- Handwritten Text Recognition Techniques 2
- Multimodal Machine Learning Applications 2
- Co-authors
- Simon Clematide (10 shared papers)Jasmine Lorenzini (3 shared papers)Hanspeter Kriesi (2 shared papers)Bruno Wüest (2 shared papers)Thomas Drugman (2 shared papers)Arnaud Joly (1 shared paper)Alexis Moinet (2 shared papers)Ray Li (1 shared paper)
- Journals
- American Behavioral Scientist (1 paper)Theory and applications of categories (1 paper)Zurich Open Repository and Archive (University of Zurich) (10 papers)Interspeech 2022 (2 papers)
- Partner nations
- SwitzerlandItalyUnited States
In The Last Decade
Peter Makarov
16 papers receiving 106 citations
Peers
Comparison fields: 5 of 24
- Artificial Intelligence 108
- General Social Sciences 7
- Computer Vision and Pattern Recognition 19
- Communication 4
- Signal Processing 5
Countries citing papers authored by Peter Makarov
This map shows the geographic impact of Peter Makarov'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 Peter Makarov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Makarov more than expected).
Fields of papers citing papers by Peter Makarov
This network shows the impact of papers produced by Peter Makarov. 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 Peter Makarov. The network helps show where Peter Makarov may publish in the future.
Co-authors
The 11 scholars most cited alongside Peter Makarov, 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 | 2018 | 20 | |
| 2 | 2018 | 20 | |
| 3 | 2021 | 18 | |
| 4 | 2017 | 9 | |
| 5 | 2020 | 9 | |
| 6 | 2022 | 8 | |
| 7 | 2018 | 7 | |
| 8 | 2021 | 6 | |
| 9 | 2020 | 5 | |
| 10 | 2016 | 5 | |
| 11 | 2022 | 5 | |
| 12 | Automated Acquisition of Patterns for Coding Political Event Data: Two Case Studies | 2018 | 4 |
| 13 | 2021 | 4 | |
| 14 | UZH at TAC KBP 2017: Event Nugget Detection via Joint Learning with Softmax-Margin Objective. | 2017 | 2 |
| 15 | 2022 | 2 | |
| 16 | 2015 | 2 |
About Peter Makarov
Peter Makarov is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, General Social Sciences, Communication and Molecular Biology, having authored 16 papers that have together received 126 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (11 papers), Topic Modeling (10 papers), Speech Recognition and Synthesis (4 papers), Computational and Text Analysis Methods (3 papers), Handwritten Text Recognition Techniques (2 papers), Speech and dialogue systems (2 papers), Social Media and Politics (2 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Artificial Intelligence (108 citations), General Social Sciences (7 citations), Computer Vision and Pattern Recognition (19 citations), Communication (4 citations) and Signal Processing (5 citations). Peter Makarov has collaborated with scholars based in Switzerland, Italy and United States. Frequent co-authors include Simon Clematide, Jasmine Lorenzini, Hanspeter Kriesi, Bruno Wüest, Thomas Drugman, Arnaud Joly, Alexis Moinet, Ray Li, Bora Seo and Sean Miller. Their work appears in journals such as American Behavioral Scientist, Theory and applications of categories, Zurich Open Repository and Archive (University of Zurich) and Interspeech 2022.
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.