Mark Craven
About
In The Last Decade
Mark Craven
104 papers receiving 5.5k citations
Hit Papers
Peers
Comparison fields: 5 of 197
- Artificial Intelligence 3.3k
- Molecular Biology 1.3k
- Information Systems 822
- Physiology 679
- Computer Vision and Pattern Recognition 602
Countries citing papers authored by Mark Craven
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).
Fields of papers citing papers by Mark Craven
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.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 3 | |
| 3 | 1 | |
| 4 | 13 | |
| 5 | 35 | |
| 6 | 82 | |
| 7 | Detecting Speculative Language Using Syntactic Dependencies and Logistic Regression | 10 |
| 8 | Multiple-Instance Active Learning | 309 |
| 9 | Ranking Biomedical Passages for Relevance and Diversity: University of Wisconsin, Madison at TREC Genomics 2006. | 15 |
| 10 | Exploiting Zone Information, Syntactic Rules, and Informative Terms in Gene Ontology Annotation of Biomedical Documents. | 6 |
| 11 | 2 | |
| 12 | Evidence combination in biomedical natural-language processing | 11 |
| 13 | Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data | 11 |
| 14 | Representing sentence structure in hidden Markov models for information extraction | 82 |
| 15 | Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes | 3 |
| 16 | 285 | |
| 17 | Learning to extract symbolic knowledge from the World Wide Web | 443 |
| 18 | Learning Sparse Perceptrons | 18 |
| 19 | Extracting Tree-Structured Representations of Trained Networks | 341 |
| 20 | Learning to Represent Codons: A Challenge Problem for Constructive Induction. | 8 |
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.