Stephen Soderland
About
In The Last Decade
Stephen Soderland
63 papers receiving 5.9k citations
Hit Papers
Peers
Comparison fields: 5 of 138
- Artificial Intelligence 5.8k
- Information Systems 2.4k
- Management Science and Operations Research 859
- Molecular Biology 730
- Computer Networks and Communications 487
Countries citing papers authored by Stephen Soderland
This map shows the geographic impact of Stephen Soderland'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 Stephen Soderland with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen Soderland more than expected).
Fields of papers citing papers by Stephen Soderland
This network shows the impact of papers produced by Stephen Soderland. 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 Stephen Soderland. The network helps show where Stephen Soderland may publish in the future.
Co-authorship network of co-authors of Stephen Soderland
This figure shows the co-authorship network connecting the top 25 collaborators of Stephen Soderland. A scholar is included among the top collaborators of Stephen Soderland 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 Stephen Soderland. Stephen Soderland is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | University of Washington TAC-KBP 2016 System Description. | 2 |
| 2 | Combining Open IE and Distant Supervision for KBP Slot Filling. | 2 |
| 3 | Open Information Extraction to KBP Relations in 3 Hours. | 17 |
| 4 | Towards Coherent Multi-Document Summarization | 78 |
| 5 | Rel-grams: A Probabilistic Model of Relations in Text | 8 |
| 6 | Open Language Learning for Information Extraction breakdown → | 409 |
| 7 | 274 | |
| 8 | Semantic Role Labeling for Open Information Extraction | 50 |
| 9 | Machine Reading at the University of Washington | 22 |
| 10 | Extracting Sequences from the Web | 1 |
| 11 | What Is This, Anyway: Automatic Hypernym Discovery. | 70 |
| 12 | Open information extraction from the web breakdown → | 831 |
| 13 | Ambiguity Reduction for Machine Translation: Human-Computer Collaboration. | 2 |
| 14 | The use of web-based statistics to validate, information extraction | 12 |
| 15 | Methods for domain-independent information extraction from the web: an experimental comparison | 73 |
| 16 | Learning Text Patterns for Web Information Extraction and Assessment (Extended Version) | 2 |
| 17 | Learning text patterns for web information extraction and assessment | 26 |
| 18 | Building a Machine Learning Based Text Understanding System | 2 |
| 19 | Learning to extract text-based information from the World Wide Web | 126 |
| 20 | Machine Learning of Text Analysis Rules for Clinical Records | 13 |
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.