Dan Roth

30.3k total citations · 6 hit papers
463 papers, 15.8k citations indexed

About

Dan Roth is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Dan Roth has authored 463 papers receiving a total of 15.8k indexed citations (citations by other indexed papers that have themselves been cited), including 425 papers in Artificial Intelligence, 56 papers in Computer Vision and Pattern Recognition and 36 papers in Information Systems. Recurrent topics in Dan Roth's work include Topic Modeling (313 papers), Natural Language Processing Techniques (293 papers) and Machine Learning and Algorithms (60 papers). Dan Roth is often cited by papers focused on Topic Modeling (313 papers), Natural Language Processing Techniques (293 papers) and Machine Learning and Algorithms (60 papers). Dan Roth collaborates with scholars based in United States, Israel and Hong Kong. Dan Roth's 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 and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Dan Roth

440 papers receiving 14.5k citations

Hit Papers

Design challenges and misconceptions in named entity reco... 2002 2026 2010 2018 2009 2002 2023 2004 2005 250 500 750

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dan Roth United States 63 13.3k 2.5k 2.0k 1.1k 836 463 15.8k
Eduard Hovy United States 68 19.0k 1.4× 2.6k 1.1× 3.6k 1.8× 909 0.8× 1.3k 1.5× 411 22.4k
Kristina Toutanova United States 33 19.1k 1.4× 4.2k 1.7× 2.7k 1.3× 871 0.8× 1.4k 1.7× 78 21.7k
Raymond J. Mooney United States 60 11.9k 0.9× 4.3k 1.7× 3.4k 1.6× 1.3k 1.2× 1.4k 1.7× 200 16.1k
Xiaojin Zhu United States 43 8.0k 0.6× 4.1k 1.6× 1.3k 0.6× 454 0.4× 617 0.7× 151 12.6k
Percy Liang United States 44 12.2k 0.9× 3.7k 1.5× 1.6k 0.8× 386 0.3× 498 0.6× 148 14.1k
Chin-Yew Lin China 45 11.1k 0.8× 2.2k 0.9× 2.7k 1.3× 404 0.4× 563 0.7× 174 12.8k
Chris Dyer United States 47 14.7k 1.1× 2.5k 1.0× 1.8k 0.9× 580 0.5× 1.2k 1.5× 187 17.0k
Jacob Devlin United States 11 14.2k 1.1× 3.8k 1.5× 1.9k 0.9× 562 0.5× 915 1.1× 20 16.4k
Dan Klein United States 55 12.5k 0.9× 2.2k 0.9× 1.6k 0.8× 347 0.3× 1.0k 1.2× 197 14.0k

Countries citing papers authored by Dan Roth

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Dan Roth

This figure shows the co-authorship network connecting the top 25 collaborators of Dan Roth. A scholar is included among the top collaborators of Dan Roth based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dan Roth. Dan Roth is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yu, Feng, et al.. (2023). Generic Temporal Reasoning with Differential Analysis and Explanation. 12013–12029. 2 indexed citations
2.
Deutsch, Daniel & Dan Roth. (2020). SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics. 120–125. 14 indexed citations
3.
Lin, Zi, Jeremiah Zhe Liu, Zi Yang, Nan Hua, & Dan Roth. (2020). Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior. 719–730. 18 indexed citations
4.
Tsai, Chen-Tse, et al.. (2016). Illinois CCG Entity Discovery and Linking, Event Nugget Detection and Co-reference, and Slot Filler Validation Systems for TAC 2016.. Theory and applications of categories. 1 indexed citations
5.
Lu, Wei & Dan Roth. (2015). Joint Mention Extraction and Classification with Mention Hypergraphs. 857–867. 143 indexed citations
6.
Roy, Subhro & Dan Roth. (2015). Solving General Arithmetic Word Problems. 1743–1752. 165 indexed citations
7.
Sammons, Mark, Haoruo Peng, Yangqiu Song, et al.. (2015). Illinois CCG TAC 2015 Event Nugget, Entity Discovery and Linking, and Slot Filler Validation Systems. Theory and applications of categories. 9 indexed citations
8.
Roth, Dan, et al.. (2013). End-to-end coreference resolution for clinical narratives. International Joint Conference on Artificial Intelligence. 2106–2112. 4 indexed citations
9.
Rozovskaya, Alla, Mark Sammons, & Dan Roth. (2012). The UI System in the HOO 2012 Shared Task on Error Correction. North American Chapter of the Association for Computational Linguistics. 272–280. 24 indexed citations
10.
Ratinov, Lev & Dan Roth. (2012). Learning-based Multi-Sieve Co-reference Resolution with Knowledge. Empirical Methods in Natural Language Processing. 1234–1244. 31 indexed citations
11.
Lu, Wei & Dan Roth. (2012). Automatic Event Extraction with Structured Preference Modeling. Meeting of the Association for Computational Linguistics. 1. 835–844. 31 indexed citations
12.
Goldwasser, Dan & Dan Roth. (2011). Learning from natural instructions. International Joint Conference on Artificial Intelligence. 1794–1800. 26 indexed citations
13.
Wang, Dong, Tarek Abdelzaher, Hossein Ahmadi, et al.. (2011). On Bayesian interpretation of fact-finding in information networks. International Conference on Information Fusion. 1–8. 39 indexed citations
14.
Pasternack, Jeff & Dan Roth. (2010). Knowing What to Believe (when you already know something). International Conference on Computational Linguistics. 877–885. 173 indexed citations
15.
Wang, Mingwei, et al.. (2010). Integer Linear Programming in NLP - Constrained Conditional Models. North American Chapter of the Association for Computational Linguistics. 9–14. 1 indexed citations
16.
Roth, Dan & Wen-tau Yih. (2004). A Linear Programming Formulation for Global Inference in Natural Language Tasks. Defense Technical Information Center (DTIC). 1–8. 290 indexed citations
17.
Garg, Ashutosh, Sariel Har-Peled, & Dan Roth. (2002). On generalization bounds, projection profile, and margin distribution. International Conference on Machine Learning. 171–178. 14 indexed citations
18.
Khardon, Roni, Dan Roth, & Leslie G. Valiant. (1999). Relational Learning for NLP using Linear Threshold Elements. International Joint Conference on Artificial Intelligence. 2(9227). 911–917. 18 indexed citations
19.
Grove, Adam J. & Dan Roth. (1997). Linear Concepts and Hidden Variables: An Empirical Study. Neural Information Processing Systems. 10. 500–506. 2 indexed citations
20.
Roth, Dan. (1996). A connectionist framework for reasoning: reasoning with examples. National Conference on Artificial Intelligence. 1256–1261. 4 indexed citations

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.

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