Rob Koeling

1.4k citations
30 papers · 848 · h-index 15

Impact in

    • Natural Language Processing Techniques
    • Topic Modeling
    • Speech and dialogue systems
    • Semantic Web and Ontologies
    • Advanced Text Analysis Techniques
    • Text and Document Classification Technologies
    • Text Readability and Simplification

Papers in

    • Natural Language Processing Techniques 24
    • Topic Modeling 21
    • Speech and dialogue systems 9
    • Semantic Web and Ontologies 4
    • Advanced Text Analysis Techniques 3
    • Text Readability and Simplification 2
    • Biomedical Text Mining and Ontologies 5

Rob Koeling

30 papers receiving 735 citations

Peers

Rob Koeling
Comparison fields: 5 of 69
  • Artificial Intelligence 704
  • Health Information Management 21
  • Language and Linguistics 31
  • Information Systems 52
  • Molecular Biology 106
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Citations per field
00.5×2.6×
Bryan Rink · 1×
Citations per year

Countries citing papers authored by Rob Koeling

Since Specialization
Citations

This map shows the geographic impact of Rob Koeling'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 Rob Koeling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rob Koeling more than expected).

Fields of papers citing papers by Rob Koeling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rob Koeling. 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 Rob Koeling. The network helps show where Rob Koeling may publish in the future.

Co-authors

The 25 scholars most cited alongside Rob Koeling, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Rob Koeling Line = papers co-authored together Rob Koeling links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004236
2 200792
3 200562
4 200058
5 201351
6 199951
7 200148
8 200031
9 201128
10 201628
11
Using automatically acquired predominant senses for word sense disambiguation
200426
12 201620
13 201116
14 200715
15
Ranking WordNet Senses Automatically
200414
16
Annotating a corpus of clinical text records for learning to recognize symptoms automatically
201111
17 201210
18 19978
19 20098
20
Gloss-Based Semantic Similarity Metrics for Predominant Sense Acquisition
20086

About Rob Koeling

Rob Koeling is a scholar working on Artificial Intelligence, Molecular Biology, Language and Linguistics, Social Psychology and General Health Professions, having authored 30 papers that have together received 848 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (24 papers), Topic Modeling (21 papers), Speech and dialogue systems (9 papers), Biomedical Text Mining and Ontologies (5 papers), Semantic Web and Ontologies (4 papers), Advanced Text Analysis Techniques (3 papers), Text Readability and Simplification (2 papers) and Safety Warnings and Signage (1 paper). The work is most often cited by research in Artificial Intelligence (704 citations), Health Information Management (21 citations), Language and Linguistics (31 citations), Information Systems (52 citations) and Molecular Biology (106 citations). Rob Koeling has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Diana McCarthy, John Carroll, Julie Weeds, Jackie Cassell, A. Rosemary Tate, Gosse Bouma, Mark-Jan Nederhof, Gertjan van Noord, Amanda Nicholson and John L. Carroll. Their work appears in journals such as Language Resources and Evaluation, BMC Medical Research Methodology, Natural Language Engineering, Computational Linguistics and Pharmacoepidemiology and Drug Safety.

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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