Rebecka Weegar

418 total citations
23 papers, 259 citations indexed

About

Rebecka Weegar is a scholar working on Artificial Intelligence, Molecular Biology and Computer Science Applications. According to data from OpenAlex, Rebecka Weegar has authored 23 papers receiving a total of 259 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 12 papers in Molecular Biology and 3 papers in Computer Science Applications. Recurrent topics in Rebecka Weegar's work include Biomedical Text Mining and Ontologies (12 papers), Topic Modeling (12 papers) and Natural Language Processing Techniques (12 papers). Rebecka Weegar is often cited by papers focused on Biomedical Text Mining and Ontologies (12 papers), Topic Modeling (12 papers) and Natural Language Processing Techniques (12 papers). Rebecka Weegar collaborates with scholars based in Sweden, Spain and Denmark. Rebecka Weegar's co-authors include Hercules Dalianis, Karin Sundström, Maria Kvist, Arantza Casillas, Alicia Pérez, Maite Oronoz, Panagiotis Papapetrou, Jalal Nouri, Aron Henriksson and Xiu Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Biomedical Informatics.

In The Last Decade

Rebecka Weegar

22 papers receiving 252 citations

Peers

Rebecka Weegar
Comparison fields: 5 of 61
  • Artificial Intelligence 164
  • Molecular Biology 82
  • Computer Science Applications 56
  • Health Information Management 32
  • Information Systems 22
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Dimitrios P. Panagoulias Greece
Reihaneh Torkzadehmahani Denmark
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Citations per field, relative to Rebecka Weegar
Rebecka Weegar · 1×
Citations per year, relative to Rebecka Weegar
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Countries citing papers authored by Rebecka Weegar

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
# 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

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.

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