Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Knowledge Enhanced Contextual Word Representations
2019359 citationsMatthew E. Peters, Mark E Neumann et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Mark E Neumann
Since
Specialization
Citations
This map shows the geographic impact of Mark E Neumann'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 E Neumann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark E Neumann more than expected).
This network shows the impact of papers produced by Mark E Neumann. 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 E Neumann. The network helps show where Mark E Neumann may publish in the future.
Co-authorship network of co-authors of Mark E Neumann
This figure shows the co-authorship network connecting the top 25 collaborators of Mark E Neumann.
A scholar is included among the top collaborators of Mark E Neumann 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 E Neumann. Mark E Neumann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lo, Kyle, Lucy Lu Wang, Mark E Neumann, Rodney Kinney, & Daniel S. Weld. (2019). GORC: A large contextual citation graph of academic papers. arXiv (Cornell University).3 indexed citations
4.
Peters, Matthew E., Mark E Neumann, Robert Logan, et al.. (2019). Knowledge Enhanced Contextual Word Representations. 43–54.359 indexed citations breakdown →
Gardner, Matt, Mark E Neumann, Joël Grus, & Nicholas Lourie. (2018). Writing Code for NLP Research. Empirical Methods in Natural Language Processing.2 indexed citations
7.
Neumann, Mark E, Pontus Stenetorp, & Sebastian Riedel. (2016). Learning to Reason with Adaptive Computation. UCL Discovery (University College London).1 indexed citations
8.
Neumann, Mark E. (2015). The impending burden of kidney disease.. PubMed. 29(4). 8–8.1 indexed citations
9.
Neumann, Mark E. (2014). PD takes a big jump in 2014, while HHD shows progress.. PubMed. 28(10). 14, 17, 34–14, 17, 34.1 indexed citations
10.
Neumann, Mark E. (2014). Urgent-start PD: moving the therapy forward.. PubMed. 28(7). 20, 22–20, 22.1 indexed citations
11.
Neumann, Mark E. (2012). Some bright spots for home dialysis.. PubMed. 26(9). 8–8.1 indexed citations
Neumann, Mark E. (2003). Getting a good night's sleep.. PubMed. 17(7). 18–18.1 indexed citations
18.
Neumann, Mark E. (2003). Results in KEEP's first report show progress in early identification of CKD patients.. PubMed. 17(12). 84–7.2 indexed citations
19.
Neumann, Mark E, et al.. (2003). Creation of innovation by knowledge management – A case study of a learning software organisation. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft).8 indexed citations
20.
Neumann, Mark E. (2002). Losing ground. A look at the recent decline in PD therapy.. PubMed. 16(3). 46–7.1 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.