Arman Cohan

7.4k citations
78 papers · 2.4k indexed · 1 hit paper · h-index 17

Impact in

    • Topic Modeling
    • Natural Language Processing Techniques
    • Advanced Text Analysis Techniques
    • Sentiment Analysis and Opinion Mining
    • Machine Learning in Healthcare
    • Semantic Web and Ontologies

Papers in

    • Topic Modeling 43
    • Natural Language Processing Techniques 34
    • Advanced Text Analysis Techniques 12
    • Semantic Web and Ontologies 5
    • Sentiment Analysis and Opinion Mining 4
    • Text Readability and Simplification 4

Arman Cohan

63 papers receiving 2.2k citations

Hit Papers

SciBERT: A Pretrained Language Model for Scientific Text 2019 · 1.6k citations
1.6k201920262021202350010001.5k

Peers

Arman Cohan
Comparison fields: 5 of 138
  • Health Informatics 85
  • Artificial Intelligence 1.8k
  • Information Systems 279
  • General Social Sciences 40
  • Applied Psychology 51
Replace Kyle Lo with:
Kyle Lo United States
Usman Naseem Australia
Tiezheng Yu Hong Kong
Andrea Madotto Hong Kong
Jun Yan China
Ziwei Ji Hong Kong
Ting Liu China
Nayeon Lee Hong Kong
Zhijun Yin United States
Arman Cohan relative to Kyle Lo United States Kyle Lo's profile →
Citations per field
00.5×10.2×
Kyle Lo · 1×
Citations per year

Countries citing papers authored by Arman Cohan

Since Specialization
Citations

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

Fields of papers citing papers by Arman Cohan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Arman Cohan, 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 Arman Cohan Line = papers co-authored together Arman Cohan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
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About Arman Cohan

Arman Cohan is a scholar working on Artificial Intelligence, Health Informatics, Family Practice, Information Systems and Computer Science Applications, having authored 78 papers that have together received 2.4k indexed citations. Recurring topics across this work include Topic Modeling (43 papers), Natural Language Processing Techniques (34 papers), Biomedical Text Mining and Ontologies (16 papers), Advanced Text Analysis Techniques (12 papers), Mental Health via Writing (5 papers), Semantic Web and Ontologies (5 papers), Sentiment Analysis and Opinion Mining (4 papers) and Text Readability and Simplification (4 papers). The work is most often cited by research in Health Informatics (85 citations), Artificial Intelligence (1.8k citations), Information Systems (279 citations), General Social Sciences (40 citations) and Applied Psychology (51 citations). Arman Cohan has collaborated with scholars based in United States, Germany and China. Frequent co-authors include Iz Beltagy, Kyle Lo, Nazli Goharian, Andrew Yates, Xiao Wen, Sean MacAvaney, Giuseppe Carenini, Luca Soldaini, Bart Desmet and Bailey Kuehl. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Journal of the Association for Information Science and Technology, npj Digital Medicine, Nature Communications and Information Retrieval.

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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