Mark Sammons

1.2k total citations
33 papers, 631 citations indexed

About

Mark Sammons is a scholar working on Artificial Intelligence, Management Science and Operations Research and Molecular Biology. According to data from OpenAlex, Mark Sammons has authored 33 papers receiving a total of 631 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 4 papers in Management Science and Operations Research and 1 paper in Molecular Biology. Recurrent topics in Mark Sammons's work include Natural Language Processing Techniques (27 papers), Topic Modeling (26 papers) and Text Readability and Simplification (9 papers). Mark Sammons is often cited by papers focused on Natural Language Processing Techniques (27 papers), Topic Modeling (26 papers) and Text Readability and Simplification (9 papers). Mark Sammons collaborates with scholars based in United States, Israel and Italy. Mark Sammons's co-authors include Dan Roth, Fabio Massimo Zanzotto, Ido Dagan, Alla Rozovskaya, V. G. Vinod Vydiswaran, Kai-Wei Chang, Vivek Srikumar, Roland Schäfer, Felix Bildhauer and Nizar Habash and has published in prestigious journals such as Computational Linguistics, Psychotherapy Research and Language Resources and Evaluation.

In The Last Decade

Mark Sammons

32 papers receiving 567 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Sammons United States 13 583 57 53 34 24 33 631
Christian M. Meyer Germany 12 606 1.0× 70 1.2× 70 1.3× 48 1.4× 18 0.8× 35 655
Jeffrey Flanigan United States 8 578 1.0× 48 0.8× 81 1.5× 40 1.2× 9 0.4× 16 624
Hongkui Yu China 4 356 0.6× 104 1.8× 44 0.8× 36 1.1× 13 0.5× 9 430
Alla Rozovskaya United States 20 877 1.5× 87 1.5× 55 1.0× 13 0.4× 15 0.6× 41 935
Olivier Ferret France 11 437 0.7× 60 1.1× 43 0.8× 109 3.2× 37 1.5× 73 493
Raman Chandrasekar United States 8 373 0.6× 85 1.5× 34 0.6× 19 0.6× 17 0.7× 29 476
Chris Hokamp Ireland 8 512 0.9× 92 1.6× 75 1.4× 43 1.3× 50 2.1× 20 552
Rodrigo Agerri Spain 14 462 0.8× 58 1.0× 26 0.5× 31 0.9× 39 1.6× 47 536
Antske Fokkens Netherlands 10 445 0.8× 77 1.4× 34 0.6× 43 1.3× 50 2.1× 63 492
Oren Melamud Israel 8 410 0.7× 42 0.7× 44 0.8× 26 0.8× 17 0.7× 14 458

Countries citing papers authored by Mark Sammons

Since Specialization
Citations

This map shows the geographic impact of Mark Sammons'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 Sammons with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Sammons more than expected).

Fields of papers citing papers by Mark Sammons

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Sammons. 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 Sammons. The network helps show where Mark Sammons may publish in the future.

Co-authorship network of co-authors of Mark Sammons

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Sammons. A scholar is included among the top collaborators of Mark Sammons 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 Mark Sammons. Mark Sammons 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.
Stone, Matthew, Lauren M. E. Goodlad, & Mark Sammons. (2024). The Origins of Generative AI in Transcription and Machine Translation, and Why That Matters. 2(1). 3 indexed citations
2.
Yu, Xiaodong, Stephen Mayhew, Mark Sammons, & Dan Roth. (2018). On the Strength of Character Language Models for Multilingual Named Entity Recognition. 8 indexed citations
3.
Peng, Haoruo, Hao Wu, Shyam Upadhyay, et al.. (2017). UI CCG TAC-KBP2017 Submissions: Entity Discovery and Linking, and Event Nugget Detection and Co-reference.. Theory and applications of categories. 4 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.
Sammons, Mark, Christos Christodoulopoulos, Parisa Kordjamshidi, et al.. (2016). EDISON: Feature Extraction for NLP, Simplified. Language Resources and Evaluation. 4085–4092. 7 indexed citations
6.
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
7.
Wu, Hao, et al.. (2014). ILLINOISCLOUDNLP: Text Analytics Services in the Cloud. Language Resources and Evaluation. 14–21. 2 indexed citations
8.
Xiao, Cheng, Kai-Wei Chang, Mark Sammons, et al.. (2013). Illinois Cognitive Computation Group UI-CCG TAC 2013 Entity Linking and Slot Filler Validation Systems. Theory and applications of categories. 2 indexed citations
9.
Rozovskaya, Alla, Kai-Wei Chang, Mark Sammons, & Dan Roth. (2013). The University of Illinois System in the CoNLL-2013 Shared Task. 13–19. 37 indexed citations
10.
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
11.
Chang, Kai-Wei, et al.. (2012). Illinois-Coref: The UI System in the CoNLL-2012 Shared Task. Empirical Methods in Natural Language Processing. 113–117. 8 indexed citations
12.
Clarke, James, Vivek Srikumar, Mark Sammons, & Dan Roth. (2012). An NLP Curator (or: How I Learned to Stop Worrying and Love NLP Pipelines). Language Resources and Evaluation. 3276–3283. 28 indexed citations
13.
Rozovskaya, Alla, et al.. (2011). University of Illinois System in HOO Text Correction Shared Task. 263–266. 25 indexed citations
14.
Chang, Kai-Wei, et al.. (2011). Inference Protocols for Coreference Resolution. 40–44. 22 indexed citations
15.
Sammons, Mark, V. G. Vinod Vydiswaran, & Dan Roth. (2010). Ask Not What Textual Entailment Can Do for You.... Meeting of the Association for Computational Linguistics. 1199–1208. 42 indexed citations
16.
Do, Quang, et al.. (2010). Robust, Light-weight Approaches to compute Lexical Similarity. Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign). 7(2). 49–56. 27 indexed citations
17.
Sammons, Mark, V. G. Vinod Vydiswaran, Tim Vieira, et al.. (2009). Relation Alignment for Textual Entailment Recognition.. Theory and applications of categories. 21 indexed citations
18.
Roth, Dan, Mark Sammons, & V. G. Vinod Vydiswaran. (2009). A framework for entailed relation recognition. 57–57. 11 indexed citations
19.
Srikumar, Vivek, Roi Reichart, Mark Sammons, Ari Rappoport, & Dan Roth. (2008). Extraction of Entailed Semantic Relations Through Syntax-Based Comma Resolution. Meeting of the Association for Computational Linguistics. 1030–1038. 12 indexed citations
20.
Braz, Rodrigo de Salvo, Roxana Gîrju, Vasin Punyakanok, Dan Roth, & Mark Sammons. (2005). An inference model for semantic entailment in natural language. International Joint Conference on Artificial Intelligence. 1678–1679. 5 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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