Mark Ferguson
Impact in
- Artificial Intelligence top 2%
- Natural Language Processing Techniques
- Topic Modeling
- Speech and dialogue systems
- Text Readability and Simplification
- Semantic Web and Ontologies
- Advanced Text Analysis Techniques
- Algorithms and Data Compression
- Language and Linguistics top 5%
Papers in
-
- Artificial Intelligence in Games 2
- Natural Language Processing Techniques 2
- Semantic Web and Ontologies 2
- Topic Modeling 2
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- Sports Analytics and Performance 2
- Co-authors
- Ann BiesKaren KatzRobert MacIntyreMitchell P. MarcusGrace KimFabio MéndezJames Alfred WalkerNaim Dahnoun
- Journals
- Journal of Visualized Experiments (1 paper)ScholarlyCommons (University of Pennsylvania) (1 paper)Bristol Research (University of Bristol) (1 paper)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Mark Ferguson
6 papers receiving 548 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 624
- Language and Linguistics 57
- Information Systems 55
- Computer Vision and Pattern Recognition 43
- Computational Mathematics 1
Countries citing papers authored by Mark Ferguson
This map shows the geographic impact of Mark Ferguson'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 Mark Ferguson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Ferguson more than expected).
Fields of papers citing papers by Mark Ferguson
This network shows the impact of papers produced by Mark Ferguson. 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 Mark Ferguson. The network helps show where Mark Ferguson may publish in the future.
Co-authors
The 20 scholars most cited alongside Mark Ferguson, 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 | 2022 | 2 | |
| 2 | 2020 | 2 | |
| 3 | 2017 | 2 | |
| 4 | 2011 | 1 | |
| 5 | Bracketing Guidelines For Treebank II Style Penn Treebank Project | 1995 | 198 |
| 6 | 1994 | 467 |
About Mark Ferguson
Mark Ferguson is a scholar working on Artificial Intelligence, Economics and Econometrics, Cancer Research, Computer Vision and Pattern Recognition and Clinical Psychology, having authored 6 papers that have together received 672 indexed citations. Recurring topics across this work include Artificial Intelligence in Games (2 papers), Natural Language Processing Techniques (2 papers), Semantic Web and Ontologies (2 papers), Sports Analytics and Performance (2 papers), Topic Modeling (2 papers), Lung Cancer Treatments and Mutations (1 paper), Cancer Genomics and Diagnostics (1 paper) and Lung Cancer Diagnosis and Treatment (1 paper). The work is most often cited by research in Artificial Intelligence (624 citations), Language and Linguistics (57 citations), Information Systems (55 citations), Computer Vision and Pattern Recognition (43 citations) and Computational Mathematics (1 citation). Mark Ferguson has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Ann Bies, Karen Katz, Robert MacIntyre, Mitchell P. Marcus, Grace Kim, Fabio Méndez, James Alfred Walker, Naim Dahnoun, Daniel Kudenko⋆ and Sam Devlin. Their work appears in journals such as Journal of Visualized Experiments, ScholarlyCommons (University of Pennsylvania) and Bristol Research (University of Bristol).
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.