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.
An analysis of active learning strategies for sequence labeling tasks
2008626 citationsBurr Settles, Mark Cravenprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of Mark Craven'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 Craven with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Craven more than expected).
This network shows the impact of papers produced by Mark Craven. 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 Craven. The network helps show where Mark Craven may publish in the future.
Co-authorship network of co-authors of Mark Craven
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Craven.
A scholar is included among the top collaborators of Mark Craven 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 Craven. Mark Craven is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Vlachos, Andreas & Mark Craven. (2011). Search-based Structured Prediction applied to Biomedical Event Extraction. 49–57.9 indexed citations
5.
Vlachos, Andreas & Mark Craven. (2010). Detecting Speculative Language Using Syntactic Dependencies and Logistic Regression. 18–25.10 indexed citations
6.
Settles, Burr, Mark Craven, & Soumya Ray. (2007). Multiple-Instance Active Learning. Neural Information Processing Systems. 20. 1289–1296.309 indexed citations
7.
Goldberg, Andrew B., David Andrzejewski, Jurgen Van Gael, et al.. (2006). Ranking Biomedical Passages for Relevance and Diversity: University of Wisconsin, Madison at TREC Genomics 2006.. Text REtrieval Conference.15 indexed citations
8.
Settles, Burr, et al.. (2005). Classifying Biomedical Articles by Making Localized Decisions.. Text REtrieval Conference.4 indexed citations
Settles, Burr & Mark Craven. (2004). Exploiting Zone Information, Syntactic Rules, and Informative Terms in Gene Ontology Annotation of Biomedical Documents.. Text REtrieval Conference.6 indexed citations
11.
Craven, Mark, et al.. (2003). Evidence combination in biomedical natural-language processing. 25–32.11 indexed citations
12.
Craven, Mark, et al.. (2003). Hierarchical hidden Markov models for information extraction. International Joint Conference on Artificial Intelligence. 427–433.94 indexed citations
13.
Bockhorst, Joseph & Mark Craven. (2002). Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data. International Conference on Machine Learning. 43–50.11 indexed citations
14.
Ray, Soumya & Mark Craven. (2001). Representing sentence structure in hidden Markov models for information extraction. International Joint Conference on Artificial Intelligence. 1273–1279.82 indexed citations
15.
Bockhorst, Joseph & Mark Craven. (2001). Refining the structure of a stochastic context-free grammar. International Joint Conference on Artificial Intelligence. 1315–1320.2 indexed citations
16.
Craven, Mark, David Page, Jude Shavlik, Joseph Bockhorst, & Jeremy D. Glasner. (2000). Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes. International Conference on Machine Learning. 199–206.3 indexed citations
17.
Craven, Mark, Dayne Freitag, Andrew McCallum, et al.. (1998). Learning to extract symbolic knowledge from the World Wide Web. National Conference on Artificial Intelligence. 509–516.443 indexed citations
18.
Craven, Mark & Jude Shavlik. (1995). Extracting Tree-Structured Representations of Trained Networks. Neural Information Processing Systems. 8. 24–30.341 indexed citations
19.
Jackson, Jeffrey C. & Mark Craven. (1995). Learning Sparse Perceptrons. Neural Information Processing Systems. 8. 654–660.18 indexed citations
20.
Craven, Mark & Jude Shavlik. (1993). Learning to Represent Codons: A Challenge Problem for Constructive Induction.. International Joint Conference on Artificial Intelligence. 1319–1324.8 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.