Silviu Paun

518 total citations
19 papers, 324 citations indexed

About

Silviu Paun is a scholar working on Artificial Intelligence, Computer Science Applications and Statistics, Probability and Uncertainty. According to data from OpenAlex, Silviu Paun has authored 19 papers receiving a total of 324 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 6 papers in Computer Science Applications and 2 papers in Statistics, Probability and Uncertainty. Recurrent topics in Silviu Paun's work include Topic Modeling (14 papers), Natural Language Processing Techniques (9 papers) and Speech and dialogue systems (8 papers). Silviu Paun is often cited by papers focused on Topic Modeling (14 papers), Natural Language Processing Techniques (9 papers) and Speech and dialogue systems (8 papers). Silviu Paun collaborates with scholars based in United Kingdom, Italy and Germany. Silviu Paun's co-authors include Massimo Poesio, Dirk Hovy, Barbara Plank, Tommaso Fornaciari, Jon Chamberlain, Udo Kruschwitz, Juntao Yu, Bob Carpenter, Valerio Basile and Michael J. Fell and has published in prestigious journals such as Journal of Artificial Intelligence Research, Transactions of the Association for Computational Linguistics and Annual Review of Linguistics.

In The Last Decade

Silviu Paun

17 papers receiving 308 citations

Peers

Silviu Paun

AI

CSA

CVPR

IS

SPS

Hui Yin China

Dmitry Ustalov Russia

Seid Muhie Yimam Germany

Yada Pruksachatkun United States

Kilian Evang Netherlands

Dimitrios Alikaniotis United Kingdom

Andrew Caines United Kingdom

Krenare Pireva Kosovo

Amin Ahmad Pakistan

E.F. Tjong Kim Sang Netherlands

Hui Yin China

Silviu Paun

139 ×0.5

AI

35 ×0.7

CSA

25 ×0.7

CVPR

45 ×1.4

IS

52 ×1.8

SPS

Citations per year, relative to Silviu Paun Silviu Paun (= 1×) peers Hui Yin

Countries citing papers authored by Silviu Paun

Since Specialization

Citations

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

Fields of papers citing papers by Silviu Paun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Silviu Paun

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

All Works

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19 of 19 papers shown

1.

Paun, Silviu, et al.. (2024). Soft metrics for evaluation with disagreements: an assessment. 84–94.

2.

Paun, Silviu, et al.. (2023). Scoring Coreference Chains with Split-Antecedent Anaphors. Queen Mary Research Online (Queen Mary University of London). 14(2). 1–48.

3.

Paun, Silviu, et al.. (2023). Aggregating Crowdsourced and Automatic Judgments to Scale Up a Corpus of Anaphoric Reference for Fiction and Wikipedia Texts. 767–781. 3 indexed citations

4.

Paun, Silviu, Ron Artstein, & Massimo Poesio. (2022). Statistical Methods for Annotation Analysis. Queen Mary Research Online (Queen Mary University of London). 15(1). 1–217. 6 indexed citations

5.

Poesio, Massimo, et al.. (2022). Computational Models of Anaphora. Annual Review of Linguistics. 9(1). 561–587. 8 indexed citations

6.

Paun, Silviu, Ron Artstein, & Massimo Poesio. (2022). Statistical Methods for Annotation Analysis. 9 indexed citations

7.

Fornaciari, Tommaso, et al.. (2021). Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning. IT University Of Copenhagen (IT University of Copenhagen). 2591–2597. 40 indexed citations

8.

Fornaciari, Tommaso, et al.. (2021). Learning from Disagreement: A Survey. Journal of Artificial Intelligence Research. 72. 1385–1470. 67 indexed citations

9.

Yu, Juntao, Nafise Sadat Moosavi, Silviu Paun, & Massimo Poesio. (2021). Stay Together: A System for Single and Split-antecedent Anaphora Resolution. 4174–4184. 7 indexed citations

10.

Paun, Silviu & Edwin Simpson. (2021). Aggregating and Learning from Multiple Annotators. 6–9. 3 indexed citations

11.

Basile, Valerio, Michael J. Fell, Tommaso Fornaciari, et al.. (2021). We Need to Consider Disagreement in Evaluation. IT University Of Copenhagen (IT University of Copenhagen). 15–21. 42 indexed citations

12.

Fornaciari, Tommaso, et al.. (2020). A Case for Soft Loss Functions. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 8. 173–177. 26 indexed citations

13.

Yu, Juntao, Nafise Sadat Moosavi, Silviu Paun, & Massimo Poesio. (2020). Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution. 6113–6125. 6 indexed citations

14.

Bartle, Richard A., et al.. (2020). Aggregation Driven Progression System for GWAPs. 79–84. 2 indexed citations

15.

Poesio, Massimo, et al.. (2019). A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation. 1778–1789. 34 indexed citations

16.

Yu, Juntao, et al.. (2019). Progression in a Language Annotation Game with a Purpose. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 7. 77–85. 3 indexed citations

17.

Yu, Juntao, et al.. (2019). Crowdsourcing and Aggregating Nested Markable Annotations. 797–807. 3 indexed citations

18.

Paun, Silviu, Bob Carpenter, Jon Chamberlain, et al.. (2018). Comparing Bayesian Models of Annotation. Transactions of the Association for Computational Linguistics. 6. 571–585. 51 indexed citations

19.

Paun, Silviu, Jon Chamberlain, Udo Kruschwitz, Juntao Yu, & Massimo Poesio. (2018). A Probabilistic Annotation Model for Crowdsourcing Coreference. 1926–1937. 14 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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