Liam Dugan
Impact in
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- Topic Modeling
- Natural Language Processing Techniques
- Intelligent Tutoring Systems and Adaptive Learning
- Text Readability and Simplification
Papers in
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- Topic Modeling 8
- Natural Language Processing Techniques 5
- Hate Speech and Cyberbullying Detection 1
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- Multimodal Machine Learning Applications 2
- Co-authors
- Chris Callison-Burch (9 shared papers)Mark E. Bier (2 shared papers)Daphne Ippolito (3 shared papers)Sherry Shi (1 shared paper)Eleni Miltsakaki (2 shared papers)Allen L. Robinson (1 shared paper)Albert A. Presto (1 shared paper)Yue Yang (1 shared paper)
- Journals
- Environmental Science & Technology (1 paper)Journal of the American Society for Mass Spectrometry (1 paper)Findings of the Association for Computational Linguistics: ACL 2022 (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)Findings of the Association for Computational Linguistics: NAACL 2022 (1 paper)
- Partner nations
- United States
In The Last Decade
Liam Dugan
11 papers receiving 78 citations
Peers
Comparison fields: 5 of 38
- Health Informatics 3
- Artificial Intelligence 41
- Computer Science Applications 5
- Computer Vision and Pattern Recognition 13
- Health, Toxicology and Mutagenesis 7
Countries citing papers authored by Liam Dugan
This map shows the geographic impact of Liam Dugan'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 Liam Dugan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liam Dugan more than expected).
Fields of papers citing papers by Liam Dugan
This network shows the impact of papers produced by Liam Dugan. 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 Liam Dugan. The network helps show where Liam Dugan may publish in the future.
Co-authors
The 14 scholars most cited alongside Liam Dugan, 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 | 2023 | 20 | |
| 2 | 2022 | 18 | |
| 3 | 2022 | 13 | |
| 4 | 2022 | 8 | |
| 5 | 2024 | 4 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 3 | |
| 8 | 2023 | 3 | |
| 9 | 2023 | 3 | |
| 10 | 2023 | 2 | |
| 11 | 2022 | 2 |
About Liam Dugan
Liam Dugan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Management Information Systems, Information Systems and Health, Toxicology and Mutagenesis, having authored 11 papers that have together received 79 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Natural Language Processing Techniques (5 papers), Multimodal Machine Learning Applications (2 papers), Computational and Text Analysis Methods (1 paper), Business Process Modeling and Analysis (1 paper), Model-Driven Software Engineering Techniques (1 paper), Hate Speech and Cyberbullying Detection (1 paper) and Atmospheric Ozone and Climate (1 paper). The work is most often cited by research in Health Informatics (3 citations), Artificial Intelligence (41 citations), Computer Science Applications (5 citations), Computer Vision and Pattern Recognition (13 citations) and Health, Toxicology and Mutagenesis (7 citations). Liam Dugan has collaborated with scholars based in United States. Frequent co-authors include Chris Callison-Burch, Mark E. Bier, Daphne Ippolito, Sherry Shi, Eleni Miltsakaki, Allen L. Robinson, Albert A. Presto, Yue Yang, Li Zhang and Ann Yuan. Their work appears in journals such as Environmental Science & Technology, Journal of the American Society for Mass Spectrometry, Findings of the Association for Computational Linguistics: ACL 2022, Proceedings of the AAAI Conference on Artificial Intelligence and Findings of the Association for Computational Linguistics: NAACL 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.