Sarah Wiegreffe
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Information Systems
- Information Systems and Management
- Molecular Biology
- Co-authors
- Ana MarasovićMark RiedlJack HesselSwabha SwayamdiptaYejin ChoiSiyan LiNiket TandonMatthew Finlayson
- Topics
- Topic Modeling (8 papers)Explainable Artificial Intelligence (XAI) (6 papers)Natural Language Processing Techniques (5 papers)
- Journals
- arXiv (Cornell University)Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Partner nations
- United States
In The Last Decade
Sarah Wiegreffe
9 papers receiving 153 citations
Peers
Comparison fields: 5 of 24
- Artificial Intelligence 147
- Computer Vision and Pattern Recognition 19
- Information Systems 12
- Information Systems and Management 11
- Molecular Biology 8
Countries citing papers authored by Sarah Wiegreffe
This map shows the geographic impact of Sarah Wiegreffe'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 Sarah Wiegreffe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sarah Wiegreffe more than expected).
Fields of papers citing papers by Sarah Wiegreffe
This network shows the impact of papers produced by Sarah Wiegreffe. 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 Sarah Wiegreffe. The network helps show where Sarah Wiegreffe may publish in the future.
Co-authorship network of co-authors of Sarah Wiegreffe
This figure shows the co-authorship network connecting the top 25 collaborators of Sarah Wiegreffe. A scholar is included among the top collaborators of Sarah Wiegreffe 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 Sarah Wiegreffe. Sarah Wiegreffe is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 13 | |
| 8 | 60 | |
| 9 | 37 | |
| 10 | Teach Me to Explain: A Review of Datasets for Explainable NLP. | 31 |
| 11 | 10 |
About Sarah Wiegreffe
Sarah Wiegreffe is a scholar working on General Social Sciences, Artificial Intelligence and Information Systems and Management, having authored 11 papers that have together received 160 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Explainable Artificial Intelligence (XAI) (6 papers) and Natural Language Processing Techniques (5 papers). The work is most often cited by research in Artificial Intelligence (147 citations), General Social Sciences (7 citations) and Information Systems and Management (11 citations). Sarah Wiegreffe has collaborated with scholars based in United States. Frequent co-authors include Ana Marasović, Mark Riedl, Jack Hessel, Swabha Swayamdipta, Yejin Choi, Siyan Li, Niket Tandon, Matthew Finlayson, Peter E. Clark and Wenlong Zhao. Their work appears in journals such as arXiv (Cornell University) and Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
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.