Natalia Grabar

1.4k total citations
122 papers, 735 citations indexed

About

Natalia Grabar is a scholar working on Artificial Intelligence, Molecular Biology and Language and Linguistics. According to data from OpenAlex, Natalia Grabar has authored 122 papers receiving a total of 735 indexed citations (citations by other indexed papers that have themselves been cited), including 94 papers in Artificial Intelligence, 61 papers in Molecular Biology and 27 papers in Language and Linguistics. Recurrent topics in Natalia Grabar's work include Natural Language Processing Techniques (68 papers), Biomedical Text Mining and Ontologies (60 papers) and Topic Modeling (37 papers). Natalia Grabar is often cited by papers focused on Natural Language Processing Techniques (68 papers), Biomedical Text Mining and Ontologies (60 papers) and Topic Modeling (37 papers). Natalia Grabar collaborates with scholars based in France, Belgium and Ukraine. Natalia Grabar's co-authors include Thierry Hamon, Pierre Zweigenbaum, Célia Boyer, Stéfan Darmoni, Patrice Degoulet, Éric Zapletal, Arnaud Gaudinat, Fleur Mougin, Vincent Claveau and Cyril Grouin and has published in prestigious journals such as SHILAP Revista de lepidopterología, ACM Computing Surveys and Cancer Letters.

In The Last Decade

Natalia Grabar

108 papers receiving 654 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Natalia Grabar France 14 478 332 90 63 51 122 735
Samuel Bayer United States 10 274 0.6× 123 0.4× 80 0.9× 73 1.2× 15 0.3× 21 409
Cédrick Fairon Belgium 9 345 0.7× 76 0.2× 59 0.7× 31 0.5× 20 0.4× 52 465
Roy J. Byrd United States 16 783 1.6× 171 0.5× 151 1.7× 82 1.3× 28 0.5× 32 1.0k
Robert Baud Switzerland 19 764 1.6× 681 2.1× 122 1.4× 212 3.4× 58 1.1× 89 1.1k
Jon Patrick Australia 15 688 1.4× 435 1.3× 20 0.2× 110 1.7× 46 0.9× 81 964
Keith E. Campbell United States 17 745 1.6× 962 2.9× 161 1.8× 380 6.0× 73 1.4× 39 1.3k
Louise Deléger France 18 521 1.1× 429 1.3× 26 0.3× 113 1.8× 35 0.7× 46 844
Mathias Brochhausen United States 16 336 0.7× 462 1.4× 12 0.1× 107 1.7× 24 0.5× 76 710
Allen C. Browne United States 18 802 1.7× 785 2.4× 69 0.8× 143 2.3× 274 5.4× 55 1.2k
Marc Weeber Netherlands 17 594 1.2× 820 2.5× 16 0.2× 23 0.4× 20 0.4× 27 1.1k

Countries citing papers authored by Natalia Grabar

Since Specialization
Citations

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

Fields of papers citing papers by Natalia Grabar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Natalia Grabar

This figure shows the co-authorship network connecting the top 25 collaborators of Natalia Grabar. A scholar is included among the top collaborators of Natalia Grabar 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 Natalia Grabar. Natalia Grabar 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
1.
2.
Grabar, Natalia, et al.. (2020). Parallel Sentence Alignment from Biomedical Comparable Corpora. Studies in health technology and informatics. 270. 362–366. 1 indexed citations
3.
Claveau, Vincent, et al.. (2020). Supervised Learning for the ICD-10 Coding of French Clinical Narratives. Studies in health technology and informatics. 270. 427–431. 3 indexed citations
4.
Grabar, Natalia, et al.. (2020). CAS: corpus of clinical cases in French. Journal of Biomedical Semantics. 11(1). 7–7. 7 indexed citations
5.
Thiessard, Frantz, et al.. (2019). Detecting Drug Non-Compliance in Internet Fora Using Information Retrieval and Machine Learning Approaches. Studies in health technology and informatics. 264. 30–34. 3 indexed citations
6.
Grabar, Natalia & Thierry Hamon. (2018). Automatic Detection of Temporal Information in Ukrainian General-language Texts. HAL (Le Centre pour la Communication Scientifique Directe). 1–10. 3 indexed citations
7.
Grabar, Natalia, et al.. (2018). Étude de la lisibilité des documents de santé avec des méthodes d'oculométrie. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
8.
Grabar, Natalia, et al.. (2015). Semantic distance-based creation of clusters of pharmacovigilance terms and their evaluation. Journal of Biomedical Informatics. 54. 174–185. 7 indexed citations
9.
Hamon, Thierry, Natalia Grabar, Fleur Mougin, & Frantz Thiessard. (2014). Description of the POMELO System for the Task 2 of QALD-2014.. CLEF (Working Notes). 1212–1223. 4 indexed citations
10.
Grouin, Cyril, Natalia Grabar, Thierry Hamon, et al.. (2013). Eventual situations for timeline extraction from clinical reports. Journal of the American Medical Informatics Association. 20(5). 820–827. 23 indexed citations
11.
Hamon, Thierry, et al.. (2012). Semantic distance and terminology structuring methods for the detection of semantically close terms. North American Chapter of the Association for Computational Linguistics. 20–28. 2 indexed citations
12.
Maxim, Laura, et al.. (2012). Public reception of scientific uncertainty in the endocrine disrupter controversy: the case of male fertility. Journal of Risk Research. 16(6). 677–695. 20 indexed citations
13.
Hamon, Thierry & Natalia Grabar. (2010). Linguistic approach for identification of medication names and related information in clinical narratives. Journal of the American Medical Informatics Association. 17(5). 549–554. 40 indexed citations
14.
Grabar, Natalia, et al.. (2009). Comparative Study between Expert and Non-Expert Biomedical Writings: Their Morphology and Semantics. Studies in health technology and informatics. 150. 359–63. 1 indexed citations
15.
Grabar, Natalia, et al.. (2008). Automatic acquisition of synonyms from French UMLS for enhanced search of EHRs.. PubMed. 136. 809–14. 2 indexed citations
16.
Zweigenbaum, Pierre, et al.. (2003). Apprentissage de relations morphologiques en corpus. Cancer Letters. 88(2). 285–294. 8 indexed citations
17.
Grabar, Natalia, Pierre Zweigenbaum, Lina F. Soualmia, & Stéfan Darmoni. (2002). A Study of the Adequacy of User and Indexing Vocabularies in Natural Language Queries to a MeSH-indexed Health Gateway. PubMed Central. 1029–1029. 1 indexed citations
18.
Zweigenbaum, Pierre & Natalia Grabar. (2002). Restoring accents in unknown biomedical words: application to the French MeSH thesaurus. International Journal of Medical Informatics. 67(1-3). 113–126. 12 indexed citations
19.
Grabar, Natalia & Pierre Zweigenbaum. (2000). Automatic acquisition of domain-specific morphological resources from thesauri. 765–784. 8 indexed citations
20.
Grabar, Natalia & Pierre Zweigenbaum. (1999). Language-independent automatic acquisition of morphological knowledge from synonym pairs.. PubMed. 2(1-4). 112–125. 4 indexed citations

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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