Dan Roth
- Artificial Intelligence top 0.02%
- Topic Modeling 313
- Natural Language Processing Techniques 293
- Machine Learning and Algorithms 60
- Advanced Text Analysis Techniques 46
- Semantic Web and Ontologies 46
- Speech and dialogue systems 35
- Text Readability and Simplification 33
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- Multimodal Machine Learning Applications 35
- Information Systems top 0.2%
- Computer Science Applications top 0.5%
- Co-authors
- Lev RatinovWen-tau YihXin LiVasin PunyakanokAlla RozovskayaSugandha AgarwalMing‐Wei ChangJeff Pasternack
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionManagement Science and Operations Research
- Journals
- Machine Learning (10 papers)Transactions of the Association for Computational Linguistics (10 papers)Theory and applications of categories (7 papers)
- Partner nations
- United StatesIsraelHong Kong
In The Last Decade
Dan Roth
440 papers receiving 14.5k citations
Hit Papers
Peers
Comparison fields: 5 of 174
- Artificial Intelligence 13.3k
- Computer Vision and Pattern Recognition 2.5k
- Management Science and Operations Research 1.1k
- Information Systems 2.0k
- Computer Science Applications 425
Countries citing papers authored by Dan Roth
This map shows the geographic impact of Dan Roth'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 Dan Roth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Roth more than expected).
Fields of papers citing papers by Dan Roth
This network shows the impact of papers produced by Dan Roth. 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 Dan Roth. The network helps show where Dan Roth may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Dan Roth, 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 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 8 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 2 | |
| 10 | 2024 | 1 | |
| 11 | 2024 | 0 | |
| 12 | 2023 | 2 | |
| 13 | 2022 | 16 | |
| 14 | 2020 | 14 | |
| 15 | 2020 | 18 | |
| 16 | 2015 | 143 | |
| 17 | 2015 | 165 | |
| 18 | 2013 | 36 | |
| 19 | 1998 | 11 | |
| 20 | 1998 | 62 |
About Dan Roth
Dan Roth is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 463 papers that have together received 15.8k indexed citations. Recurring topics across this work include Topic Modeling (313 papers), Natural Language Processing Techniques (293 papers), Machine Learning and Algorithms (60 papers), Advanced Text Analysis Techniques (46 papers), Semantic Web and Ontologies (46 papers), Speech and dialogue systems (35 papers), Multimodal Machine Learning Applications (35 papers) and Text Readability and Simplification (33 papers). The work is most often cited by research in Artificial Intelligence (13.3k citations), Computer Vision and Pattern Recognition (2.5k citations) and Management Science and Operations Research (1.1k citations). Dan Roth has collaborated with scholars based in United States, Israel and Hong Kong. Frequent co-authors include Lev Ratinov, Wen-tau Yih, Xin Li, Vasin Punyakanok, Alla Rozovskaya, Sugandha Agarwal, Ming‐Wei Chang, Jeff Pasternack, Subhro Roy and Richard Sproat. Their work appears in journals such as Machine Learning, Transactions of the Association for Computational Linguistics, Theory and applications of categories, Language Resources and Evaluation and Artificial Intelligence.
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.