Rebecka Weegar
- Artificial Intelligence top 10%
- Molecular Biology
- Computer Science Applications top 5%
- Health Information Management top 5%
- Information Systems
- Co-authors
- Hercules DalianisKarin SundströmMaria KvistArantza CasillasAlicia PérezMaite OronozPanagiotis PapapetrouJalal Nouri
- Topics
- Biomedical Text Mining and Ontologies (12 papers)Topic Modeling (12 papers)Natural Language Processing Techniques (12 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEJournal of Biomedical Informatics
In The Last Decade
Rebecka Weegar
22 papers receiving 252 citations
Peers
Comparison fields: 5 of 61
- Artificial Intelligence 164
- Molecular Biology 82
- Computer Science Applications 56
- Health Information Management 32
- Information Systems 22
Countries citing papers authored by Rebecka Weegar
This map shows the geographic impact of Rebecka Weegar'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 Rebecka Weegar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rebecka Weegar more than expected).
Fields of papers citing papers by Rebecka Weegar
This network shows the impact of papers produced by Rebecka Weegar. 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 Rebecka Weegar. The network helps show where Rebecka Weegar may publish in the future.
Co-authorship network of co-authors of Rebecka Weegar
This figure shows the co-authorship network connecting the top 25 collaborators of Rebecka Weegar. A scholar is included among the top collaborators of Rebecka Weegar 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 Rebecka Weegar. Rebecka Weegar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 14 | |
| 2 | 6 | |
| 3 | 67 | |
| 4 | 4 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 26 | |
| 8 | 13 | |
| 9 | 1 | |
| 10 | 3 | |
| 11 | 6 | |
| 12 | 28 | |
| 13 | A Sentiment model for Swedish with automatically created training data and handlers for language specific traits | 1 |
| 14 | The impact of simple feature engineering in multilingual medical NER | 1 |
| 15 | Finding Cervical Cancer Symptoms in Swedish Clinical Text using a Machine Learning Approach and NegEx. | 17 |
| 16 | HEALTH BANK - A Workbench for Data Science Applications in Healthcare | 41 |
| 17 | 12 | |
| 18 | 1 | |
| 19 | Visual Entity Linking: A Preliminary Study | 3 |
| 20 | Passage retrieval in a question answering system. | 1 |
About Rebecka Weegar
Rebecka Weegar is a scholar working on Artificial Intelligence, Computer Science Applications and Software, having authored 23 papers that have together received 259 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (12 papers), Topic Modeling (12 papers) and Natural Language Processing Techniques (12 papers). The work is most often cited by research in Health Informatics (22 citations), Computer Science Applications (56 citations) and Health Information Management (32 citations). Rebecka Weegar has collaborated with scholars based in Sweden, Spain and Denmark. Frequent co-authors include Hercules Dalianis, Karin Sundström, Maria Kvist, Arantza Casillas, Alicia Pérez, Maite Oronoz, Panagiotis Papapetrou, Jalal Nouri, Aron Henriksson and Xiu Li. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Biomedical Informatics.
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.