Artem Shelmanov

707 total citations
32 papers, 254 citations indexed

About

Artem Shelmanov is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Artem Shelmanov has authored 32 papers receiving a total of 254 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Control and Systems Engineering. Recurrent topics in Artem Shelmanov's work include Topic Modeling (23 papers), Natural Language Processing Techniques (21 papers) and Advanced Text Analysis Techniques (4 papers). Artem Shelmanov is often cited by papers focused on Topic Modeling (23 papers), Natural Language Processing Techniques (21 papers) and Advanced Text Analysis Techniques (4 papers). Artem Shelmanov collaborates with scholars based in Russia, United Arab Emirates and Australia. Artem Shelmanov's co-authors include Alexander Panchenko, Dmitry V. Dylov, Irina Fedulova, Chris Biemann, Mikhail Arkhipov, Oleg Y. Rogov, Ivan Smirnov, Evgenii Tsymbalov, Maxim S. Panov and Maxim Panov and has published in prestigious journals such as Scientific Reports, Language Resources and Evaluation and Transactions of the Association for Computational Linguistics.

In The Last Decade

Artem Shelmanov

28 papers receiving 239 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Artem Shelmanov Russia	9	189	33	22	22	20	32	254
Valentin Malykh Russia	7	200 1.1×	21 0.6×	64 2.9×	9 0.4×	20 1.0×	39	249
Xiangru Tang United States	7	234 1.2×	31 0.9×	32 1.5×	6 0.3×	18 0.9×	22	317
Shizhe Diao Hong Kong	9	208 1.1×	75 2.3×	14 0.6×	5 0.2×	25 1.3×	23	280
Alexander R. Fabbri United States	11	314 1.7×	30 0.9×	42 1.9×	25 1.1×	46 2.3×	20	380
Amit Pimpalkar India	7	110 0.6×	44 1.3×	11 0.5×	12 0.5×	34 1.7×	32	214
Braden Hancock United States	7	139 0.7×	25 0.8×	31 1.4×	5 0.2×	36 1.8×	9	191
Baoyu Jing United States	10	227 1.2×	31 0.9×	12 0.5×	31 1.4×	42 2.1×	18	276
Sahar Vahdati Germany	9	149 0.8×	34 1.0×	37 1.7×	8 0.4×	34 1.7×	28	224
Shuofei Qiao China	7	149 0.8×	25 0.8×	8 0.4×	5 0.2×	27 1.4×	12	200
Jan Trienes Germany	4	190 1.0×	21 0.6×	14 0.6×	13 0.6×	6 0.3×	6	250

Countries citing papers authored by Artem Shelmanov

Since Specialization

Citations

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

Fields of papers citing papers by Artem Shelmanov

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Artem Shelmanov

This figure shows the co-authorship network connecting the top 25 collaborators of Artem Shelmanov. A scholar is included among the top collaborators of Artem Shelmanov 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 Artem Shelmanov. Artem Shelmanov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Shelmanov, Artem, et al.. (2025). A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads for Hallucination Detection in LLM Outputs. Open MIND. 35700–35719.

Shelmanov, Artem, et al.. (2024). Vikhr: Constructing a State-of-the-art Bilingual Open-Source Instruction-Following Large Language Model for Russian. 189–199. 2 indexed citations

Wang, Yuxia, P.M. Ivanov, Artem Shelmanov, et al.. (2024). M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection. ISTI Open Portal. 3964–3992. 10 indexed citations

Shelmanov, Artem, Hamdy Mubarak, Evgenii Tsymbalov, et al.. (2024). Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification. 9367–9385. 5 indexed citations

Ivanov, P.M., et al.. (2024). SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection. ISTI Open Portal. 2057–2079. 4 indexed citations

Shelmanov, Artem, et al.. (2023). Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability?. 744–770. 2 indexed citations

Panov, Maxim S., et al.. (2023). Efficient Out-of-Domain Detection for Sequence to Sequence Models. 1430–1454. 3 indexed citations

Rogov, Oleg Y., et al.. (2023). Medical image captioning via generative pretrained transformers. Scientific Reports. 13(1). 4171–4171. 48 indexed citations

Shelmanov, Artem, et al.. (2023). Text augmentation for semantic frame induction and parsing. Language Resources and Evaluation. 58(2). 363–408. 1 indexed citations

10.

Panchenko, Alexander, et al.. (2023). LM-Polygraph: Uncertainty Estimation for Language Models. 446–461. 4 indexed citations

11.

Va, Karlov, et al.. (2022). Active Learning for Abstractive Text Summarization. 5128–5152. 5 indexed citations

12.

Miftahutdinov, Zulfat, et al.. (2022). RuCCoN: Clinical Concept Normalization in Russian. Findings of the Association for Computational Linguistics: ACL 2022. 239–245. 1 indexed citations

13.

Shelmanov, Artem, et al.. (2022). Open Information Extraction from Texts: Part III. Question Answering over an Automatically Constructed Knowledge Base. Scientific and Technical Information Processing. 49(6). 416–426.

14.

Dylov, Dmitry V., et al.. (2022). ALToolbox: A Set of Tools for Active Learning Annotation of Natural Language Texts. 406–434. 2 indexed citations

15.

Shelmanov, Artem, et al.. (2022). Neural entity linking: A survey of models based on deep learning. Semantic Web. 13(3). 527–570. 52 indexed citations

16.

Shelmanov, Artem, et al.. (2021). NB-MLM: Efficient Domain Adaptation of Masked Language Models for Sentiment Analysis. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 9114–9124. 8 indexed citations

17.

Fedulova, Irina, et al.. (2019). Multitask and Multimodal Neural Network Model for Interpretable Analysis of X-ray Images. 1601–1604. 6 indexed citations

18.

Shelmanov, Artem, et al.. (2019). Towards the Data-driven System for Rhetorical Parsing of. 82–87. 2 indexed citations

19.

Shelmanov, Artem, et al.. (2019). Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates. 619–628. 14 indexed citations

20.

Shelmanov, Artem, et al.. (2017). Semantic-Syntactic Analysis for Question Answering and Definition Extraction. Scientific and Technical Information Processing. 44(6). 412–423. 2 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.

Explore authors with similar magnitude of impact