Matthew R. Gormley
- Artificial Intelligence top 5%
- Topic Modeling 21
- Natural Language Processing Techniques 21
- Speech Recognition and Synthesis 3
- Text and Document Classification Technologies 3
- Speech and dialogue systems 2
- Bayesian Modeling and Causal Inference 2
- Machine Learning and Algorithms 2
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- Software Engineering Research 2
- Co-authors
- Benjamin Van DurmeCourtney NapolesMark DredzeJason EisnerGraham NeubigBarun PatraMo YuThomas Schaaf
- Journals
- Cognitive Affective & Behavioral Neuroscience (1 paper)Transactions of the Association for Computational Linguistics (1 paper)Development (1 paper)
- Partner nations
- United StatesBelgiumAustria
In The Last Decade
Matthew R. Gormley
28 papers receiving 346 citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 318
- Obstetrics and Gynecology 24
- Computer Vision and Pattern Recognition 40
- General Social Sciences 5
- Health Information Management 7
Countries citing papers authored by Matthew R. Gormley
This map shows the geographic impact of Matthew R. Gormley'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 Matthew R. Gormley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew R. Gormley more than expected).
Fields of papers citing papers by Matthew R. Gormley
This network shows the impact of papers produced by Matthew R. Gormley. 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 Matthew R. Gormley. The network helps show where Matthew R. Gormley may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Matthew R. Gormley, 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 | 2 | |
| 2 | 2023 | 7 | |
| 3 | 2022 | 5 | |
| 4 | 2022 | 4 | |
| 5 | 2021 | 30 | |
| 6 | 2021 | 13 | |
| 7 | 2020 | 1 | |
| 8 | 2019 | 51 | |
| 9 | 2019 | 4 | |
| 10 | 2018 | 7 | |
| 11 | 2018 | 3 | |
| 12 | 2017 | 30 | |
| 13 | 2017 | 6 | |
| 14 | 2016 | 1 | |
| 15 | 2015 | 0 | |
| 16 | 2015 | 28 | |
| 17 | 2014 | 4 | |
| 18 | Nonconvex Global Optimization for Latent-Variable Models | 2013 | 9 |
| 19 | Shared Components Topic Models | 2012 | 9 |
| 20 | Non-Expert Correction of Automatically Generated Relation Annotations | 2010 | 11 |
About Matthew R. Gormley
Matthew R. Gormley is a scholar working on Artificial Intelligence, Information Systems, Obstetrics and Gynecology, Cultural Studies and Literature and Literary Theory, having authored 29 papers that have together received 380 indexed citations. Recurring topics across this work include Topic Modeling (21 papers), Natural Language Processing Techniques (21 papers), Speech Recognition and Synthesis (3 papers), Text and Document Classification Technologies (3 papers), Speech and dialogue systems (2 papers), Bayesian Modeling and Causal Inference (2 papers), Software Engineering Research (2 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Artificial Intelligence (318 citations), Obstetrics and Gynecology (24 citations), Computer Vision and Pattern Recognition (40 citations), General Social Sciences (5 citations) and Health Information Management (7 citations). Matthew R. Gormley has collaborated with scholars based in United States, Belgium and Austria. Frequent co-authors include Benjamin Van Durme, Courtney Napoles, Mark Dredze, Jason Eisner, Graham Neubig, Barun Patra, Mo Yu, Thomas Schaaf, Yang Liu and Hua Cheng. Their work appears in journals such as Cognitive Affective & Behavioral Neuroscience, Transactions of the Association for Computational Linguistics, Development, Journal of Visualized Experiments and North American Chapter of the Association for Computational Linguistics.
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.